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1
Al-Arbi, Salem. "Condition monitoring of gear systems using vibration analysis." Thesis, University of Huddersfield, 2012. http://eprints.hud.ac.uk/id/eprint/17821/.
Full textAbstract:
It is often impractical to measure vibrations directly at /or close to their sources when condition monitoring gearbox systems. It is common to measure the vibration distant from the source due to limited access to the component which is to be monitored. In addition, operating the gearbox under different loads and speeds also produces vibration signals within different components. Vibration measured in this way may be significantly distorted by the effect of signal transmission paths and interference from other sources. Therefore, suppression of distortions is a key issue for remote measurement based condition monitoring. In this research work, the influences of transducer locations and operating conditions on the vibration signal have been investigated on a typical gearbox transmission system for the detection of faults induced within the gearbox. Vibration signals corresponding to a healthy (baseline) and faulty conditions on two-stage helical gearbox at various load and speed levels were recorded. The baseline vibration data were examined using conventional methods in the time, frequency and the joint time-frequency domains, and are referenced for comparison with more advanced methods. Several parameters have been proposed for monitoring gear condition locally (gearbox casing) including time, frequency, and joint time-frequency domain representation. The results show that traditional signal processing techniques were insufficient for revealing fault detection information due to the low signal to noise ratio (SNR). This research also presents a mathematical model for the simulation of vibration signals in order to further understand the source of the vibration. The model represents a two stage gear system using a suitable stiffness function to represent the forces acting between each pair of gears. Rotational stiffness and damping are also used to simulate the angular motion of the gears and shafts. Results show that the frequency spectrum of acceleration outputs from the model take the expected form with peaks at the meshing frequency and associated harmonics. Furthermore, if the stiffness function between the first pair of gears is simulated with a broken tooth, and various degrees of damage, outputs from the simulation have similar sideband effects to the signals produced in the experimental investigation. In addition, the model also demonstrates that variation of load and speed produces a corresponding effect to that seen in the experiments. Consequently, although relatively simple, the mathematical model can be used to explain vibration mechanisms in real gearbox systems used in condition monitoring. Time synchronous averaging (TSA) has been applied to the vibration signals from the gearbox to remove random noise combined with the raw signal. The angular domain signal, the order spectrum and the order-frequency presentation were used to characterise gearbox vibration in these new domains in more detail. Results obtained following TSA were compared with those obtained through conventional analysis from waveform characteristics, spectrum patterns and corresponding feature parameters under different operating loads and fault conditions. In addition, continuous wavelet transform (CWT) of TSA was also compared with the conventional CWT results of raw signals to further characterise vibrations. As part of this research study, the vibration transmission path has been estimated using the frequency response function (FRF) technique. A response based estimation method has been developed to revise the base path and adapted to operating conditions for more accurate fault estimation. Both theoretical analysis and test results showed that improved diagnosis when the path information was included in vibration signal processing and feature selection. Finally, the vibration data recorded from the two accelerometers located on the gearbox casing and motor flange were analyzed using different signal processing methods to investigate the effect of path transmission (transducer location) on the detection and diagnosis of the seeded gear tooth faults. Results from the angular domain, the order spectrum and the order-frequency analysis are presented to demonstrate use of these techniques for fault detection in gearboxes and that the effect of path transmissions can be observed on the vibration signals. Results showed that CWT of the TSA signal could be used to detect and indicate the severity of the gear damage effectively even if vibration signals originated from a remote motor flange.
2
Moussa, Wael. "Thermography-Assisted Bearing Condition Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31379.
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Abstract
Despite the large amount of research work in condition based maintenance and condition monitoring methods, there is still a need for more reliable and accurate methods.
The clear evidence of that need is the continued dependence on time based maintenance, especially for critical applications such as turbomachinery and airplane engines. The lack of accurate condition monitoring systems could lead to not only the unexpected failures as well as the resulting hazards and repair costs, but also a huge waste of material and time because of unnecessary replacement due to false alarms and unnecessary repair and maintenance.
Temperature change is a phenomenon that accompanies every dynamic activity in the universe. However, it has not been adequately exploited for mechanical system condition monitoring. The reason is the slow response of current temperature monitoring systems compared to other condition monitoring methods such as vibration analysis. Many references inferred that the change in temperature is not sensible until approaching the end of the monitored component life and even the whole system life (Kurfess, et al., 2006; Randall, 2011; Patrick, et al., March 7-14, 2009).
On the other hand, the most commonly used condition monitoring method, i.e., vibration analysis, is not free from pitfalls. Although vibration analysis has shown success in detecting some bearing faults, for other faults like lubrication problems and gradual wear it is much less effective. Also, it does not give a reliable indication of fault severity for many types of bearing faults.
The advancement of thermography as a temperature monitoring tool encourages the reconsideration of temperature monitoring for mechanical system fault detection. In addition to the improved accuracy and responsiveness, it has the advantage of non-contact monitoring which eliminates the need for complex sensor mounting and wiring especially for rotating components.
Therefore, in current studies the thermography-based monitoring method is often used either as a distinct method or as a complementary tool to vibration analysis in an integrated condition monitoring system. The main objectives of this study are hence to:
1. Define heat sources in the rolling element bearings and overview two of the most famous bearing temperature calculation methods.
2. Setup a bearing test rig that is equipped with both vibration and temperature monitoring systems.
3. Develop a temperature calculation analytical model for rolling element bearing that include both friction calculation and heat transfer models. The friction calculated by the model will be compared to that calculated using the pre-defined empirical methods. The heat transfer model is used for bearing temperature calculation that will be compared to the experimental measurement using different temperature monitoring devices.
4. Propose a new in-band signal enhancement technique, based on the synchronous averaging technique, Autonomous Time Synchronous Averaging (ATSA) that does not need an angular position measuring device. The proposed method, in addition to the Spectral Kurtosis based band selection, will be used to enhance the bearing envelope analysis.
5. Propose a new method for classification of the bearing faults based on the fault severity and the strength of impulsiveness in vibration signals. It will be used for planning different types of tests using both temperature and vibration methods.
6. Develop and experimentally test a new technique to stimulate the bearing temperature transient condition. The technique is supported by the results of finite element modeling and is used for bearing temperature condition monitoring when the bearing is already running at thermal equilibrium condition.
3
Esu, Ozak O. "Vibration-based condition monitoring of wind turbine blades." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21679.
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Significant advances in wind turbine technology have increased the need for maintenance through condition monitoring. Indeed condition monitoring techniques exist and are deployed on wind turbines across Europe and America but are limited in scope. The sensors and monitoring devices used can be very expensive to deploy, further increasing costs within the wind industry. The work outlined in this thesis primarily investigates potential low-cost alternatives in the laboratory environment using vibration-based and modal testing techniques that could be used to monitor the condition of wind turbine blades. The main contributions of this thesis are: (1) the review of vibration-based condition monitoring for changing natural frequency identification; (2) the application of low-cost piezoelectric sounders with proof mass for sensing and measuring vibrations which provide information on structural health; (3) the application of low-cost miniature Micro-Electro-Mechanical Systems (MEMS) accelerometers for detecting and measuring defects in micro wind turbine blades in laboratory experiments; (4) development of an in-service calibration technique for arbitrarily positioned MEMS accelerometers on a medium-sized wind turbine blade. This allowed for easier aligning of coordinate systems and setting the accelerometer calibration values using samples taken over a period of time; (5) laboratory validation of low-cost modal analysis techniques on a medium-sized wind turbine blade; (6) mimicked ice-loading and laboratory measurement of vibration characteristics using MEMS accelerometers on a real wind turbine blade and (7) conceptualisation and systems design of a novel embedded monitoring system that can be installed at manufacture, is self-powered, has signal processing capability and can operate remotely. By applying the conclusions of this work, which demonstrates that low-cost consumer electronics specifically MEMS accelerometers can measure the vibration characteristics of wind turbine blades, the implementation and deployment of these devices can contribute towards reducing the rising costs of condition monitoring within the wind industry.
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Abboud, Dany. "Vibration-based condition monitoring of rotating machines in nonstationary regime." Thesis, Lyon, INSA, 2015. http://www.theses.fr/2015ISAL0106/document.
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Dans les dernières décennies, la surveillance vibratoire des machines tournantes a acquis un intérêt particulier fournissant une aide efficace pour la maintenance dans l'industrie. Aujourd'hui, de nombreuses techniques efficaces sont bien établies, ancrées sur des outils puissants offerts notamment par la théorie des processus cyclostationnaires. Cependant, toutes ces techniques reposent sur l'hypothèse d’un régime de fonctionnement (c.à.d. vitesse et/ou charge) constant ou éventuellement fluctuant d’une façon stationnaire. Malheureusement, la plupart des machines surveillées dans l'industrie opèrent sous des régimes non stationnaires afin de remplir les tâches pour lesquelles elles ont été conçues. Dans ce cas, ces techniques ne parviennent pas à analyser les signaux vibratoires produits. Ce problème a occupé la communauté scientifique dans la dernière décennie et des techniques sophistiquées de traitement du signal ont été conçues pour faire face à la variabilité du régime. Mais ces tentatives restent limitées, dispersées et généralement peu soutenues par un cadre théorique. Le principal objectif de cette thèse est de combler partiellement cette lacune sur la base d'une formalisation théorique du sujet et d’un développement systématique de nouveaux outils de traitement du signal. Dans ce travail, la non-stationnarité du régime est limitée à celle de la vitesse— c.à.d. vitesse variable et charge constante— supposée connue a priori. Afin d'atteindre cet objectif, la méthodologie adoptée consiste à étendre le cadre cyclostationnaire avec ses outils dédiés. Nous avons élaboré cette stratégie en distinguant deux types de signatures. Le premier type comprend des signaux déterministes connus comme cyclostationnaires au premier ordre. La solution proposée consiste à généraliser la classe cyclostationnaire au premier ordre à la classe cyclo-non-stationnaire au premier ordre qui comprend des signaux déterministes en vitesse variable. Le second type comprend des signaux aléatoires périodiquement corrélés connus comme cyclostationnaires au deuxième ordre. Trois visions différentes mais complémentaires ont été proposées pour traiter les variations induites par la non-stationnarité de la vitesse de fonctionnement. La première adopte une approche cyclostationnaire angle\temps, la seconde une solution basée sur l'enveloppe et la troisième une approche cyclo-non-stationnaire (au second ordre). De nombreux outils ont été conçus dont les performances ont été testées avec succès sur des signaux vibratoires réels et simulésIn the last decades, vibration-based condition monitoring of rotating machine has gained special interest providing an efficient aid for maintenance in the industry. Nowadays, many efficient techniques are well-established, rooted on powerful tools offered in particular by the theory of cyclostationary processes. However, all these techniques rely on the assump-tion of constant— or possibly fluctuating but stationary— operating regime (i.e. speed and/or load). Unfortunately, most monitored machines used in the industry operate under nonstationary regimes in order to fulfill the task for which they have been designed. In this case, these techniques fail in analyzing the produced vibration signals. This issue, therefore, has occupied the scientific committee in the last decade and some sophisticated signal processing techniques have been conceived to deal with regime variability. But these works remain limited, dispersed and generally not supported by theoretical frameworks. The principal goal of this thesis is to partially fill in this gap on the basis of a theoretical formalization of the subject and a systematic development of new dedicated signal processing tools. In this work, the nonstationarity of the regime is confined to that of the speed— i.e. variable speed and constant load, assumed to be known a priori. In order to reach this goal, the adopted methodology consists in extending the cyclostationary framework together with its dedicated tools. We have elaborated this strategy by distinguishing two types of signatures. The first type includes deterministic waveforms known as first-order cyclostationary. The proposed solution consists in generalizing the first-order cyclostationary class to the more general first-order cyclo-non-stationary class which enfolds speed-varying deterministic signals. The second type includes random periodically-correlated waveforms known as second-order cyclostationary. Three different but complementary visions have been proposed to deal with the changes induced by the nonstationarity of the operating speed. The first one adopts an angle\time cyclostationary approach, the second one adopts an envelope-based solution and the third one adopts a (second-order) cyclo-non-stationary approach. Many tools have been conceived whose performances have been successfully tested on simulated and real vibration signals
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Mirhadizadeh, S. A. "Monitoring hydrodynamic bearings with acoustic emission and vibration analysis." Thesis, Cranfield University, 2012. http://dspace.lib.cranfield.ac.uk/handle/1826/7888.
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Acoustic emission (AE) is one of many available technologies for condition
health monitoring and diagnosis of rotating machines such as bearings. In
recent years there have been many developments in the use of Acoustic
Emission technology (AET) and its analysis for monitoring the condition of
rotating machinery whilst in operation, particularly on high speed machinery.
Unlike conventional technologies such as oil analysis, motor current signature
analysis (MCSA) and vibration analysis, AET has been introduced due to its
increased sensitivity in detecting the earliest stages of loss of mechanical
integrity.
This research presents an experimental investigation that is aimed at
developing a mathematical model and experimentally validating the influence of
operational variables such as film thickness, rotational speed, load, power loss,
and shear stress for variations of load and speed conditions, on generation of
acoustic emission in a hydrodynamic bearing. It is concluded that the power
losses of the bearing are directly correlated with acoustic emission levels. With
exponential law, an equation is proposed to predict power losses with
reasonable accuracy from an AE signal.
This experimental investigation conducted a comparative study between AE
and Vibration to diagnose the rubbing at high rotational speeds in the
hydrodynamic bearing. As it is the first known attempt in rotating machines. It
has been concluded, that AE parameters such as amplitude, can perform as a
reliable and sensitive tool for the early detection of rubbing between surfaces of
a hydrodynamic bearing and high speed shaft.
The application of vibration (PeakVue) analysis was introduced and compared
with demodulation. The results observed from the demodulation and PeakVue
techniques were similar in the rubbing simulation test. In fact, some defects on
hydrodynamic bearings would not have been seen in a timely manner without
the PeakVue analysis.In addition, the application of advanced signal processing and statistical
methods was established to extract useful diagnostic features from the acquired
AE signals in both time and frequency domain. It was also concluded that the
use of different signal processing methods is often necessary to achieve
meaningful diagnostic information from the signals. The outcome would largely
contribute to the development of effective intelligent condition monitoring
systems which can significantly reduce the cost of plant maintenance.
To implement these main objectives, the Sutton test rig was modified to assess
the capability of AET and vibration analysis as an effective tool for the detection
of incipient defects within high speed machine components (e.g. shafts and
hydrodynamic bearings).
The first chapter of this thesis is an introduction to this research and briefly
explains motivation and the theoretical background supporting this research.
The second and third chapters, summarise the relevant literature to establish
the current level of knowledge of hydrodynamic bearings and acoustic emission,
respectively. Chapter 4 describes methodologies and the experimental
arrangements utilized for this investigation. Chapter 5 discusses different NDT
diagnosis. Chapter 6 reports on an experimental investigation applied to
validate the relationship between AET on operational rotating machines, such
as film thickness, speed, load, power loss, and shear stress. Chapter 7 details
an investigation which compares the applicability of AE and vibration
technologies in monitoring a rubbing simulation on a hydrodynamic bearing.
6
Chen, Zhidong. "Machine condition monitoring based on the analysis of transient vibration signals." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0016/NQ58400.pdf.
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Barbini, Leonardo. "Techniques for condition monitoring using cyclo-non-stationary signals." Thesis, University of Bath, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.761025.
Full textAbstract:
Condition based maintenance is becoming increasingly popular in many industrial contexts, offering substantial savings and minimising accidental damage. When applied to rotating machinery, its most common tool is vibration analysis, which relies on well-established mathematical models rooted in the theory of cyclo-non-stationary processes. However, the extraction of diagnostic information from the real world vibration signals is a delicate task requiring the application of sophisticated signal processing techniques, tailored for specific machines operating under restricted conditions. Such difficulty in the current state of the art of vibration analysis forces the industry to apply methods with reduced diagnostic capabilities but higher adaptability. However in doing so most of the potential of vibration analysis is lost and advanced techniques become of use only for academic endeavours. The aim of this document is to reduce the gap between industrial and academic applications of condition monitoring, offering ductile and automated tools which still show high detection capabilities. Three main lines of research are presented in this document. Firstly, the implementation of stochastic resonance in an electrical circuit to enhance directly the analog signal from an accelerometer, in order to lower the computational requirements in the next digital signal processing step. Secondly, the extension of already well-established digital signal processing techniques, cepstral prewhitening and spectral kurtosis, to a wider range of operating conditions, proving their effectiveness in the case of non-stationary speeds. Thirdly, the main contribution of the thesis: the introduction of two novel techniques capable of separating the vibrations of a defective component from the overall vibrations of the machine, by means of a threshold in the amplitude spectrum. After the separation, the cyclic content of the vibration signal is extracted and the thresholded signals provide an enhanced detection. The two proposed methods, phase editing and amplitude cyclic frequency decomposition, are both intuitive and of low computational complexity, but show the same capabilities as more sophisticated state of the art techniques. Furthermore, all these tools have been successfully tested on numerically simulated signals as well as on real vibration data from different machinery, lasting from laboratory test rigs to wind turbines drive-trains and aircraft engines. So in conclusion, the proposed techniques are a promising step toward the full exploitation of condition based maintenance in industrial contexts.
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Calabrese, Francesca. "Vibration Monitoring and Intelligent Diagnosis Tools for Condition-Based Maintenance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.
Find full textAbstract:
Ogni impianto di produzione è caratterizzato da periodi di operatività, nei quali funziona correttamente, e da periodi di fermo, dovuti alla presenza di guasti o all’esigenza di effettuare attività volte a ristabilire il suo normale comportamento. L’obiettivo principale della funzione manutenzione è minimizzare i periodi di fermo impianto, al fine di renderlo il più disponibile possibile.
Attualmente, la manutenzione basata su condizione (CBM) è una delle più politiche più efficaci adottate dalle industrie. Essa è basata sul monitoraggio di diversi parametri della macchina che ne riflettono lo stato di salute. Tra i parametri più utilizzati si trovano i segnali di vibrazione. La CBM può essere implementata attraverso quattro passi principali: raccolta dati, analisi dei segnali, diagnostica e prognostica. Tale procedura prende il nome di Prognostic Health Monitoring (PHM).
La necessità di analizzare la grande mole di dati raccolta attraverso il vibration monitoring richiede l’utilizzo di metodi sviluppati nell’ambito della teoria statistica e del data mining, che si pongono l’obiettivo di riconoscere andamenti regolari all’interno di grandi insiemi di dati, al fine di generare conoscenza funzionale al processo decisionale manutentivo. In particolare, i modelli di classificazione, come alberi decisionali, algoritmi K-NN, reti neurali e Support Vector Machine, costituiscono un potente strumento per la diagnostica. Tali modelli, sulla base del PHM, vengono applicati dopo la fase di analisi dei segnali, che consiste principalmente nell’estrazione di features sia nel dominio del tempo che nel dominio tempo-frequenza.
Il risultato principale ottenuto consiste nell’aver verificato un incremento delle performance, in termini di accuratezza, della classificazione dello stato di salute di un componente, dovuto all’introduzione dell’analisi nel dominio tempo-frequenza e allo sviluppo dei nuovi metodi “intelligenti”.
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Sihra, Tarsem Singh. "The application of dual channel analysis techniques for on-line vibration monitoring of mining processes." Thesis, University of the West of Scotland, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386687.
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Nembhard, Adrian. "On-bearing vibration response integration for condition monitoring of rotating machinery." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/onbearing-vibration-response-integration-for-condition-monitoring-of-rotating-machinery(f713f156-11f3-4e10-846e-0b9b709f0ff9).html.
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Vibration-based fault diagnosis (FD) with a simple spectrum can be complex, especially when considering FD of rotating machinery with multiple bearings like a multi-stage turbine. Various studies have sought to better interpret fault spectra, but the process remains equivocal. Consequently, it has been accepted that the simple spectra requires support from additional techniques, such as orbit analysis. But even orbit analysis can be inconclusive. Though promising, attempts at developing viable methods that rival the failure coverage of spectrum analysis without gaining computational complexity remain protracted. Interestingly, few researchers have developed FD methods for transient machine operation, however, these have proven to be involved. Current practices limit vibration data to a single machine, which usually requires a large unique data history. However, if sharing of data between similar machines with different foundations was possible, the need for unique histories would be mitigated. From readily available works, this has not been encountered. Therefore, a simple but robust vibration-based approach is warranted. In light of this, a novel on-bearing vibration response integration approach for condition monitoring of shaft-related faults irrespective of speed and foundation type is proposed in the present study. Vibration data are acquired at different speeds for: a baseline, unbalance, bow, crack, looseness, misalignment, and rub conditions on three laboratory rigs with dynamically different foundations, namely: rigid, flexible support 1 (FS1) and flexible support 2 (FS2). Testing is done on the rigid rig set up first, then FS1, and afterwards FS2. Common vibration features are computed from the measured data to be input to the proposed approach for further processing. First, the proposed approach is developed through its application to a machine at a steady speed in a novel Single-speed FD technique which exploits a single vibration sensor per bearing and fusion of features from different bearings for FD. Initially, vibration features are supplemented with bearing temperature readings with improved classification compared to vibration features alone. However, it is observed that temperature readings are insensitive to faults on the FS1 and FS2 rigs, when compared to vibration features, which are standardised for consistent classification on the different rigs tested. Thus, temperature is not included as a final feature. The observed fault classifications on the different rigs at different speeds with the standardised vibration features are encouraging. Thereafter, a novel Unified Multi-speed FD technique that is based on the initial proposed approach and which works by fusion of vibration features from different bearings at different speeds in a single analysis step for FD is proposed. Experiments on the different rigs repeatedly show the novel Multi-speed technique to be suitable for transient machine operation. Then, a novel generic Multi-foundation Technique (also based on the proposed approach) that allows sharing of vibration data of a wide range of fault conditions between two similarly configured machines with similar speed operation but different foundations is implemented to further mitigate data requirements in the FD process. Observations made with the rigs during steady and transient speed tests show this technique is applicable in situations where data history is available on one machine but lacking on the other. Comparison of experimental results with results obtained from theoretical simulations indicates the approach is consistent. Thus, the proposed approach has the potential for practical considerations.
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Cease, Barry T. "Multi-feature signature analysis for bearing condition monitoring using neural network methodology." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/19328.
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Elbarghathi, Fathalla. "Condition monitoring of helical gearboxes based on the advanced analysis of vibration signals." Thesis, University of Huddersfield, 2016. http://eprints.hud.ac.uk/id/eprint/30647/.
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Condition monitoring of rotating machinery and machine systems has attracted extensive researches, particularly the detection and diagnosis of machine faults in their early stages to minimise maintenance cost and avoid catastrophic breakdowns and human injuries. As an efficient mechanical system, helical gearbox has been widely used in rotating machinery such as wind turbines, helicopters, compressors and internal combustion engines and hence its vibration condition monitoring is attracting substantial research attention worldwide. However, the vibration signals from a gearbox are usually contaminated by background noise and influenced by operating conditions. It is usually difficult to obtain symptoms of faults at the early stage of a fault. This study focus on developing effective approaches to the detection of early stage faults in an industrial helical gearbox. In particular, continuous wavelet transformation (CWT) has been investigated in order to select an optimal wavelet to effectively represent the vibration signals for both noise reduction and fault signature extraction. To achieve this aim, time synchronous average (TSA) is used as a tool for preliminary noise reduction and mathematical models of a gearbox transmission system is developed for characterising fault signatures. The performance of three different wavelet families was compared and henceforth a criterion and method for the selection of the most discerning has been established. It has been found that the wavelet that gives the highest RMS value for the baseline vibration signal will show the greatest difference between baseline and gearbox vibration with a fault presence. Comparison of the three wavelets families shows that the Daubechies order 1 can give best performance for feature extraction and fault detection and fault quantification. However, there are limitations that undermine CWT application to fault detection, in particular the difficulty in selecting a suitable wavelet function. A major contribution of this research programme is to demonstrate a possible route on how to overcome this deficiency. An adaptive Morlet wavelet transform method has been proposed based on information entropy optimization for analysing the vibration signal and detecting and quantifying the faults seeded into the helical gearbox. This research has also developed a nonlinear dynamic model of the two-stage helical gearbox involving time–varying mesh stiffness and transmission error. Based on experimental data collected with different operating loads and the simulating results vibration signatures for gear faults are fully understood and hence confirms the CWT based scheme for signal enhancement. These results also indicate that the dynamic model can be used in studying gear faults and would be useful in developing gear fault monitoring techniques.
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Faghidi, Hamid. "Non-parametric and Non-filtering Methods for Rolling Element Bearing Condition Monitoring." Thèse, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/30689.
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Rolling element bearings are one of the most significant elements and frequently-used components in mechanical systems. Bearing fault detection and diagnosis is important for preventing productivity loss and averting catastrophic failures of mechanical systems. In industrial applications, bearing life is often difficult to predict due to different application conditions, load and speed variations, as well as maintenance practices. Therefore, reliable fault detection is necessary to ensure productive and safe operations.
Vibration analysis is the most widely used method for detection and diagnosis of bearing malfunctions. A measured vibration signal from a sensor is often contaminated by noise and vibration interference components. Over the years, many methods have been developed to reveal fault signatures, and remove noise and vibration interference components.
Though many vibration based methods have been proposed in the literature, the high frequency resonance (HFR) technique is one of a very few methods have received certain industrial acceptance. However, the effectiveness of the HFR methods depends, to a great extent, on some parameters such as bandwidth and centre frequency of the fault excited resonance, and window length. Proper selection these parameters is often a knowledge-demanding and time-consuming process. In particular, the filter designed based on the improperly selected bandwidth and center frequency of the fault excited resonance can filter out the true fault information and mislead the detection/diagnosis decisions. In addition, even if these parameters can be selected properly at beginning of each process, they may become invalid in a time-varying environment after a certain period of time. Hence, they may have to be re-calculated and updated, which is again a time-consuming and error-prone process. This undermines the practical significance of the above methods for online monitoring of bearing conditions.
To overcome the shortcomings of existing methods, the following four non-parametric and non-filtering methods are proposed:
1. An amplitude demodulation differentiation (ADD) method,
2. A calculus enhanced energy operator (CEEO) method,
3. A higher order analytic energy operator (HO_AEO) approach, and
4. A higher order energy operator fusion (HOEO_F) technique.
The proposed methods have been evaluated using both simulated and experimental data.
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Zolkiewski, G. M. "Leak detection and condition monitoring of process valves by vibration analysis as a basis for condition based maintenance." Thesis, University of Manchester, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314626.
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Liu, Chao-Shih. "Analysis, approach and assessment of vibration criteria in shipboard machinery condition monitoring and diagnostics." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA276583.
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Wang, Wei Ji. "Gearbox condition monitoring and early damage diagnosis by two and three dimensional vibration signal analysis." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334237.
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Rzeszucinski, Pawel. "Development of reliable vibration-based condition indicators and their data fusion for the robust health diagnosis of gearboxes." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/development-of-reliable-vibrationbased-condition-indicators-and-their-data-fusion-for-the-robust-health-diagnosis-of-gearboxes(fa25db2f-89a5-420f-ba56-68ef7da874f9).html.
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Performing condition monitoring related tasks on any machinery is an essential element of their rational maintenance. Endeavours to detect an incipient fault within a system serve multiple purposes from increasing the safety of people responsible for operating the machines through decreasing the running and operational costs, allowing time to plan for the inevitable repairs and making sure that the downtime of the machine is kept to an absolute minimum. All these tasks gain extra importance in a case when machines are operated in dangerous conditions putting people's lives in potential jeopardy - for instance in the field of operating a helicopter. The robust assessment of the condition of gearboxes used by helicopters has recently been given an increased attention due to a number of accidents which followed an undetected drive train component failure. The majority of the on-board mounted condition monitoring systems use vibration response signals which are specifically processed to obtain a single number which is representative of a condition of a given monitored drive train component. Those signal processing methods are called Condition Indicators (CIs). There are a number of such CIs which are already in use and they seem to adequately indicate faults in most of the cases. However in a number of instances it has been observed that the most popular parameters like Crest Factor or FM4 failed to dependably reflect the true condition of the gear causing serious accidents, some of which resulted in a number of lives being lost. For this reason the presented research is focused on investigating the limitations of the existing CIs and designing a set of improved CIs. The development process is based on overcoming the drawbacks of thetechniques used in existing CIs combined with the intelligence gathered while analysing the acceleration vibration signals which contained a gear or a bearing fault. Five new CIs are proposed and the details of their design are documented. Both the existing and the proposed CIs are applied on the available, uncorrelated datasets. The results of the comparison show that the newly developed CIs are capable of indicating a gear or a bearing fault in a more robust and dependable fashion. Each proposed CI alone may not be the most robust indicator of the actual condition of the monitored component hence the output from all proposed CIs is combined into a single indication through use of a novel data fusion model. The Combined CI created based on the data fusion model is observed to be more robust compared to each CI alone, hence it may increase the confidence level of the decision making routine and is expected to decrease the number of false alarms. The methods of the existing CIs, the proposed CIs and the data fusion techniques as well as the results of the comparison between the different approaches are present in this thesis.
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Hassin, Osama A. A. "Condition monitoring of journal bearings for predictive maintenance management based on high frequency vibration analysis." Thesis, University of Huddersfield, 2017. http://eprints.hud.ac.uk/id/eprint/34161/.
Full textAbstract:
Journal bearings are widely used as rotor supports in many machinery systems such as engines, motors, turbines and huge pumps. The journal bearing is simply designed, highly efficient, has a long life, low cost and doesn’t fail easily. Based on preventive maintenance strategies, many monitoring techniques are developed for monitoring journal bearings such as lubricant analysis, vibration analysis, noise and acoustic emission analysis. Vibration monitoring techniques have been developed and it can be implemented online or offline without interrupting the machine operations. The vibration phenomena in a journal bearing is complicated which combined between different types of signals created by different sources. To understand this phenomenon, a vibration model is established for fault diagnosis, which includes not only conventional hydrodynamic forces but also excitations of both asperity collisions and churns. However, mis-operations and oil degradation in the journal bearings might cause unexpected and sudden failure which is risky in machines and operators. Consequently, clustering technique is used to investigate into vibration responses of journal bearings for identifying different lubrication regimes as categorised by the classic Stribeck curve. High frequency clustering allows different lubricant oils and different lubrication regimes to be identified appropriately, providing feasible ways for online monitoring of bearing conditions. Additionally, modulation signal bispectrum magnitude results represent the nonlinear vibration responses with two distinctive bifrequency patterns corresponding to instable lubrication and asperity interactions. Using entropy measures, these instable operating conditions are classified to be the low loads cases. Furthermore, average MSB magnitudes are used to differentiate the asperity interactions between asperity collisions and the asperity churns. In addition, the oil starvation of a journal bearing has been found by MSB analysis that the instable frequency can affect the measured vibration responses. Moreover, the structural resonances in the high frequency range can better reflect the separation of different oil levels under wide operating conditions. Finally, As a result of worn bearings, shaft fluctuation increases and asperity collisions decreases. Thus a worn bearing is not all the time good because of instability.
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Nour, Fathy E. "The analysis of vibration signals during induction motor starting transients with a view to early fault detection." Thesis, Robert Gordon University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294706.
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Bleakley, Steven Shea, and steven bleakley@qr com au. "Time Frequency Analysis of Railway Wagon Body Accelerations for a Low-Power Autonomous Device." Central Queensland University, 2006. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20070622.121515.
Full textAbstract:
This thesis examines the application of the techniques of Fourier spectrogram and wavelet analysis to a low power embedded microprocessor application in a novel railway and rollingstock monitoring system.
The safe and cost effective operation of freight railways is limited by the dynamic performance of wagons running on track. A monitoring system has been proposed comprising of low cost wireless sensing devices, dubbed Health Cards, to be installed on every wagon in the fleet. When marshalled into a train, the devices would sense accelerations and communicate via radio network to a master system in the locomotive. The integrated system would provide online information for decision support systems.
Data throughput was heavily restricted by the network architecture, so significant signal analysis was required at the device level. An electronics engineering team at Central Queensland University developed a prototype Health Card, incorporating a 27MHz microcontroller and four dual axis accelerometers. A sensing arrangement and online analysis algorithms were required to detect and categorise dynamic events while operating within the constraints of the system.
Time-frequency analysis reveals the time varying frequency content of signals, making it suitable to detect and characterise transient events. With efficient algorithms such as the Fast Fourier Transform, and Fast Wavelet Transform, time-frequency analysis methods can be implemented on a low power, embedded microcontroller.
This thesis examines the application of time-frequency analysis techniques to wagon body acceleration signals, for the purpose of detecting poor dynamic performance of the wagon-track system. The Fourier spectrogram is implemented on the Health Card prototype and demonstrated in the laboratory. The research and algorithms provide a foundation for ongoing development as resources become available for system testing and validation.
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Lee, Sang-Kwon. "Adaptive signal processing and higher order time frequency analysis for acoustic and vibration signatures in condition monitoring." Thesis, University of Southampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242731.
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Tanzariello, Roberta. "Condition Monitoring of a Belt-Based Transmission System for Comau Racer3 Robots." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14354/.
Full textAbstract:
This project has been developed in collaboration with Comau Robotics S.p.a and the main goal is the development in China of an Health Monitoring Pro-cess using vibration analysis. This project is connected to the activity of Cost Reduction carried out by the PD Cost Engineering Department in China.
The Project is divided in two part:
1. Data Acquisition
2. Data Analysis
An Automatic Acquisition of the moni.log file is carried out and is discussed in Chapter 1. As for the Data Analysis is concerned a data driven approach is considered and developed in frequency domain through the FFT transform and in time domain using the Wavelet transform.
In Chapter 2 a list of the techiques used nowadays for the Signal Analysis and the Vibration Monitoring is shown in time domain, frequency domain and time-frequency domain.
In Chapter 3 the state of art of the Condition Monitoring of all the possible ma-chinery part is carried out from the evaluation of the spectrum of the current and speed.
In Chapter 4 are evaluated disturbances that are not related to a fault but be-long to a normal behaviour of the system acting on the measured forces. Motor Torque Ripple and Output Noise Resolution are disturbance dependent on ve-locity and are mentioned in comparison to the one related to the configuration of the Robot.
In Chapter 5 a particular study case is assigned: the noise problem due to belt-based power transmission system of the axis three of a Racer 3 Robot in Endu-rance test. The chapter presents the test plan done including all the simula-tions.
In Chapter 6 all the results are shown demostrating how the vibration analysis carried out from an external sensor can be confirmed looking at the spectral content of the speed and the current.
In the last Chapter the final conclusions and a possible development of this thesis are presented considering both a a Model of Signal and a Model Based approach.
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Sandström, Tobias. "Condition Monitoring of Ceramic Ball Bearings in an Engine Testing Dynamometer." Thesis, KTH, Maskinkonstruktion (Inst.), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183126.
Full textAbstract:
The choice of the topic addressed in this thesis aims to improve the service and maintenance on ceramic ball bearings in a specific test dynamometer and through an engineering approach develop tools for condition monitoring. The company connected to this thesis, AVL, is the world's largest privately owned company for development, simulation and testing technology of powertrains for passenger cars, trucks and large engines. Engine testing is a critical part of the business at AVL Sweden and unexpected bearing failure can result in long repair times and great economic losses due to loss of the testing time. In short terms, the methodological approach followed the following steps; first a thorough information retrieval regarding bearings and analysis was conducted. The search was deepened around areas such as hybrid ball bearings, bearing failure mechanisms, bearing defect frequencies, signal analysis and condition monitoring. After this a table for bearing damage detection was developed and a “step by step” guidance for condition monitoring. The tools where afterwards verified by simple testing to detect complications within the chosen system. The existing condition monitoring system that is used today revealed weaknesses as it lacked the feature of taking preventive measures. The system that is based on temperature measurements isn’t satisfactory enough, especially when it’s missing visual clarity. Service and maintenance according to specifications from the manufacturer should be scheduled to ensure operational and guarantees. Currently mounted accelerometers on the housing of the Dynas3 engine should be connected for collecting data and the total sum of energy should be calculated for simple monitoring of historical progression. This should be done by following the guidance in order to ensure proper data acquisition. The best way to implement condition monitoring showed to be by performing multi-parameter monitoring. The design of the condition monitoring system is highly connected to what to monitor and at what stage. One main consideration to keep in mind is that it’s very rare that manufacturing defects are the reason for bearing failure. Instead it derives from improper storage, transport, handling or dimensional errors and even in some cases by improperly implemented force analysis prior to bearing selection.Huvudämnet som behandlas i detta examensarbete syftar till att förbättra service och underhåll på keramiska kullager i en viss testdynamometer och genom ett ingenjörsmässigt tillvägagångsätt utveckla verktyg för tillståndsövervakning. Företaget som är ansluten till detta examensarbete är AVL som är världens största privatägda företag för utveckling, simulering och testteknik för drivlinor för personbilar, lastbilar och stora motorer. Motorprovning är en viktig del av verksamheten vid AVL Sverige, och ett oväntat lagerhaveri kan leda till långa reparationstider och stora ekonomiska förluster på grund av utebliven test tid. I korta termer följde den metod som använts följande steg, först genomfördes en grundlig informationssökning om lager och tillhörande analyser. Efter det fördjupades sökande kring områden som hybrida kullager, lagerskademekanismer, frekvenser kopplade till lagerskador, signalanalys och tillståndsövervakning. Efter detta framställdes en tabell för detektering av lagerskador, samt en ”steg för steg” guide för tillståndsövervakning. Verktygen för tillståndsövervakning kontrolleras efteråt, genom att enkla tester genomfördes för att upptäcka komplikationer inom det valda systemet. Det övervakningssystem som används idag avslöjade svagheter genom att sakna funktionen att vidta förebyggande åtgärder. System som är baserat på temperaturmätningar är inte tillräckligt tillfredsställande, speciellt när det saknar en visuell tydlighet. Den service och underhåll som enligt tillverkarens föreskrifter påvisas bör planeras för att säkerställa drift och garantier. Nuvarande monterade accelerometrar fästa vid motorhöljet bör anslutas för att insamla data, och den totala summan av energin bör beräknas för en enkel övervakning av det historiska utvecklingsförloppet. Detta bör göras genom att följa de riktlinjer som framställts för att säkerställa korrekt datainsamling. Det bäst passande sättet att genomföra tillståndsövervakning på i detta fall visade sig vara att utföra multiparameterövervakning. Framställningen av tillståndsövervakningssystemet är starkt förknippat med vad som skall övervakas och i vilket skede. En huvudsaklig bidragande faktor att komma ihåg är att det är mycket ovanligt att fabrikationsfel är orsaken till lagerhaveri. Istället härstammar haveriet från felaktig förvaring, transportering, hantering eller dimensioneringsfel och i vissa fall av felaktigt genomförd kraftanalys inför lagerval.
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Svensson, Gustav, and Mischa Huisman. "Concepts for a suitable condition based monitoring system for a planetary gearbox." Thesis, Linnéuniversitetet, Institutionen för maskinteknik (MT), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-74787.
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In the trends of technical improvements and automatization is it important for companies to keep up with the developments to be competitive on the market. SwePart Transmissions AB is a company that manufacture and develop gearboxes for the currently growing robot arms industry and the main task with this study is to investigate how to apply condition based monitoring on a new gearbox from the company. The work considers vibration analysis and testing new ideas in the oil analysis field. The tests that were performed are based on measuring the difference in impedance or magnetic field due to the increasement of wear. The results of the tests are not clear. This thesis is the beginning of a big project and therefore lies the value of this work in the new ideas and suggestions for further work.
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Alshimmeri, Fiasael. "Diagnosis of low-speed bearing degradation using acoustic emission techniques." Thesis, Cranfield University, 2017. http://dspace.lib.cranfield.ac.uk/handle/1826/12324.
Full textAbstract:
It is widely acknowledged that bearing failures are the primary reason for
breakdowns in rotating machinery. These failures are extremely costly,
particularly in terms of lost production. Roller bearings are widely used in
industrial machinery and need to be maintained in good condition to ensure the
continuing efficiency, effectiveness, and profitability of the production process.
The research presented here is an investigation of the use of acoustic emission
(AE) to monitor bearing conditions at low speeds.
Many machines, particularly large, expensive machines operate at speeds below
100 rpm, and such machines are important to the industry. However, the
overwhelming proportion of studies have investigated the use of AE techniques
for condition monitoring of higher-speed machines (typically several hundred
rpm, or even higher). Few researchers have investigated the application of these
techniques to low-speed machines (<100 rpm), This PhD addressed this
omission and has established which, of the available, AE techniques are suitable
for the detection of incipient faults and measurement of fault growth in low-speed
bearings.
The first objective of this research program was to assess the applicability of AE
techniques to monitor low-speed bearings. It was found that the measured
statistical parameters successfully monitored bearing conditions at low speeds
(10-100 rpm).
The second objective was to identify which commonly used statistical parameters
derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify
the onset of a fault in either race. It was found that the change in AE amplitude
and AE RMS could identify the presence of a small fault seeded into either the
inner or the outer races. However, the severe attenuation of the signal from the
inner race meant that, while AE amplitude and RMS could readily identify the
incipient fault, kurtosis and the AE counts could not. Thus, more attention needs
to be given to analysing the signal from the inner race. The third objective was to identify a measure that would assess the degree of
severity of the fault. However, once the defect was established, it was found that
of the parameters used only AE RMS was sensitive to defect size.
The fourth objective was to assess whether the AE signal is able to detect defects
located at either the centre or edge of the outer race of a bearing rotating at low
speeds. It is found that all the measured AE parameters had higher values when
the defect was seeded in the middle of the outer race, possibly due to the shorter
path traversed by the signal between source and sensor which gave a lower
attenuation than when the defect was on the edge of the outer race. Moreover,
AE can detect the defect at both locations, which confirmed the applicability of
the AE to monitor the defects at any location on the outer race.
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Obiora, Obinna Chukwuemeka. "Wireless condition monitoring to reduce maintenance resources in the Escravos–Gas–To–Liquids plant, Nigeria / Obiora, O.C." Thesis, North-West University, 2011. http://hdl.handle.net/10394/7040.
Full textAbstract:
The purpose of this research is to reduce maintenance resources and improve Escravos–Gas–to–Liquids plant availability (EGTL) in Escravos, Nigeria using wireless condition monitoring. Secondary to the above is to justify the use of this technology over other conventional condition monitoring methods in petrochemical plants with specific reference to cost, reliability and security of the system.
Wireless and continuous condition monitoring provides the means to evaluate current conditions of equipment and detect abnormalities. It allows for corrective measures to be taken to prevent upcoming failures. Continuous monitoring and event recording provides information on the energized equipment's response to normal and emergency conditions.
Wireless/remote monitoring helps to coordinate equipment specifications and ratings, determine the real limits of the monitored equipment and optimize facility operations. Bentley N, (2005).
Using wireless techniques eliminate any need for special cables and wires with lower installation costs if compared to other types of condition monitoring systems.
In addition to this, wireless condition monitoring works well under difficult conditions in strategically important locations. The Escravos gas–to–liquid plant in Nigeria, located in a remote and offshore area where accommodation and space for offices is a factor for monitoring plant conditions in every office, is a typical example. Wireless technology for condition monitoring of energized equipment is applicable to both standalone and remote systems.
In the research work of Meyer and Brambley (2002), they characterized the current problem with regards to cost effectiveness and availability of wireless condition monitoring. Maintenance of rotating equipment provides probability estimates of the total impact of the problem, cost implication of plant equipment maintenance and describes a generic system in which these developing technologies are used to provide real–time wireless/remote condition monitoring for rotating main air compressor (MAC) units and their components as a case study.
Costs with today’s technology are provided and future costs are estimated, showing that benefits will greatly exceed costs in many cases, particularly if low–cost wireless monitoring is used.
With management trends such as “re–engineering” and “downsizing” of the available workforce, wireless condition–monitoring of critical machines has been given more importance as a way to ensure quality production with fewer personnel. Wireless condition–monitoring using inexpensive wireless communication technology frees up existing plant maintenance personnel work on machines that are signaling problems and focusing the maintenance efforts away from attempting to work on a large population of machines to only those machines requiring immediate attention.
Lloyd and Buddy (200) suggested that Point–to–point wireless data transmission systems, an excellent example of recent technological advances in communication systems, are now practical and cost–effective for industrial use. While both complex infrastructures and complex protocols are required for cellular communications, non– cellular communication systems, such as the point–to–point wireless data transmission system example, require no elaborate infrastructure.
Limited research was done on the immediate benefits of implementing wireless condition monitoring systems in plants. All papers on the subject have been drawn up by manufacturers of such equipment. This research will thus also deliver a "third–party" perspective on the effectiveness of such devices, justifying their impact on data gathering security, cost and reliability.Thesis (M.Ing. (Development and Management Engineering))--North-West University, Potchefstroom Campus, 2012.
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Elnady, Maged Elsaid. "On-shaft vibration measurement using a MEMS accelerometer for faults diagnosis in rotating machines." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/onshaft-vibration-measurement-using-a-mems-accelerometer-for-faults-diagnosis-in-rotating-machines(cf9b9848-972d-49ff-a6b0-97bef1ad0e93).html.
Full textAbstract:
The healthy condition of a rotating machine leads to safe and cheap operation of almost all industrial facilities and mechanical systems. To achieve such a goal, vibration-based condition monitoring has proved to be a well-accepted technique that detects incipient fault symptoms. The conventional way of On-Bearing Vibration Measurement (OBVM) captures symptoms of different faults, however, it requires a relatively expensive setup, an additional space for the auxiliary devices and cabling in addition to an experienced analyst. On-Shaft Vibration Measurement (OSVM) is an emerging method proposed to offer more reliable Faults Diagnosis (FD) tools with less number of sensors, minimal processing time and lower system and maintenance costs. The advancement in sensor and wireless communications technologies enables attaching a MEMS accelerometer with a miniaturised wireless data acquisition unit directly to the rotor without altering the machine dynamics. In this study, OSVM is analysed during constant speed and run-up operations of a test rig. The observations showed response modulation, hence, a Finite Element (FE) analysis has been carried out to help interpret the experimental observations. The FE analysis confirmed that the modulation is due to the rotary motion of the on-shaft sensor. A demodulation method has been developed to solve this problem. The FD capability of OSVM has been compared to that of OBVM using conventional analysis where the former provided more efficient diagnosis with less number of sensors. To incorporate more features, a method has been developed to diagnose faults based on Principal Component Analysis and Nearest Neighbour classifier. Furthermore, the method is enhanced using Linear Discriminant Analysis to do the diagnosis without the need for a classifier. Another faults diagnosis method has been developed that ensures the generalisation of extracted faults features from OSVM data of a specific machine to similar machines mounted on different foundations.
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Sambayi, Patrick Mukenyi Kataku. "Drill wear monitoring using instantaneous angular speed : a comparison with conventional technologies used in drill monitoring systems." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/32282.
Full textAbstract:
Most drill wear monitoring research found in the literature is based on
conventional vibration technologies. However, these conventional approaches still have
not attracted real interest from manufacturers for multiples of reasons: some of these
techniques are not practical and use complicated Tool Condition Monitoring (TCM)
systems with less value in industry. In addition, they are also prone to give spurious drill
deterioration warnings in industrial environments. Therefore, drills are normally replaced
at estimated preset intervals, sometimes long before they are worn or by expertise
judgment.
Two of the great problems in the implementation of these systems in drilling are:
the poor signal-to-noise ratio and the lack of system-made sensors for drilling, as is
prevalent in machining operations with straight edge cutters. In order to overcome the
noise problems, many researchers recommend advanced and sophisticated signal
processing while the work of Rehorn et al. (2005) advises the following possibilities to
deal with the lack of commercial system-made sensors:
Some research should be directed towards developing some form of
instrumented tool for drill operations.
Since the use of custom-made sensors is being ignored in drilling operations,
effort should be focused on intelligent or innovative use of available sensor
technology.
It is expected that the latter could minimize implementation problems and allows an
optimal drill utilization rate by means of modern and smart sensors.
In addition to the accelerometer sensor commonly used in conventional methods,
this work has considered two other sensor-based methods to monitor the drill wear
indirectly. These methods entail the use of an instrumented drill with strain gauges to
measure the torque and the use of an encoder to measure the Instantaneous Angular
Speed (IAS). The signals from these sensors were analyzed using signal processing
techniques such as, statistical parameters, Fast Fourier Transform (FFT), and a
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preliminary Time-Frequency (TF) analysis. A preliminary investigation has revealed that
the use of a Regression Analysis (RA) based on a higher order polynomial function can
very well follow and give prognosis of the development of the monitored parameters.
The experimental investigation has revealed that all the above monitoring systems
are sensitive to the deterioration of the drill condition. This work is however particularly
concerned with the use of IAS on the spindle of the drill, compared to conventional
monitoring systems for drill condition monitoring. This comparison reveals that the IAS
approach can generate diagnostic information similar to vibration and torque
measurements, without some of the instrumentation complications. This similitude seems
to be logical, as it is well known that the increase of friction between the drill and workpiece
due to wear increase the torque and consequently it should reduce or at least affect
the spindle rotational speed.
However, the use of a drill instrumented with a strain gauge is not practical,
because of the inconvenience it causes on production machines. By contrast, the IAS
could be measured quite easily by means of an encoder, a tachometer or some other smart
rotational speed sensors. Thus, one could take advantage of advanced techniques in
digital time interval analysis applied to a carrier signal from a multiple pulse per
revolution encoder on the rotating shaft, to improve the analysis of chain pulses. As it
will be shown in this dissertation, the encoder resolution does not sensibly affect the
analysis. Therefore, one can easily replace encoders by any smart transducers that have
become more popular in rotating machinery. Consequently, a non-contact transducer for
example could effectively be used in on-line drill condition monitoring such as the use of
lasers or time passage encoder-based systems.
This work has gained from previous research performed in Tool Condition
Monitoring TCM, and presents a sensor that is already available in the arsenal of sensors
and could be an open door for a practical and reliable sensor in automated drilling.
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In conclusion, this dissertation strives to answer the following question: Which one of
these methods could challenge the need from manufacturers by monitoring and
diagnosing drill condition in a practical and reliable manner? Past research has
sufficiently proved the weakness of conventional technologies in industry despite good
results in the laboratory. In addition, delayed diagnosis due to time-consuming data
processing is not beneficial for automated drilling, especially when the drill wears rapidly
at the end of its life. No advanced signal processing is required for the proposed
technique, as satisfactory results are obtained using common time domain signal
processing methods. The recommended monitoring choice will definitely depend on the
sensor that is practical and reliable in industry.Dissertation (MEng)--University of Pretoria, 2012.
gm2013
Mechanical and Aeronautical Engineering
MEng
Unrestricted
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Ahnesjö, Henrik. "Fault detection of planetary gearboxes in BLDC-motors using vibration and acoustic noise analysis." Thesis, Uppsala universitet, Institutionen för elektroteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425966.
Full textAbstract:
This thesis aims to use vibration and acoustic noise analysis to help a production line of a certain motor type to ensure good quality. Noise from the gearbox is sometimes present and the way it is detected is with a human listening to it. This type of error detection is subjective, and it is possible for human error to be present. Therefore, an automatic test that pass or fail the produced Brush Less Direct Current (BLDC)-motors is wanted. Two measurement setups were used. One was based on an accelerometer which was used for vibration measurements, and the other based on a microphone for acoustic sound measurements. The acquisition and analysis of the measurements were implemented using the data acquisition device, compactDAQ NI 9171, and the graphical programming software, NI LabVIEW. Two methods, i.e., power spectrum analysis and machine learning, were used for the analyzing of vibration and acoustic signals, and identifying faults in the gearbox. The first method based on the Fast Fourier transform (FFT) was used to the recorded sound from the BLDC-motor with the integrated planetary gearbox to identify the peaks of the sound signals. The source of the acoustic sound is from a faulty planet gear, in which a flank of a tooth had an indentation. Which could be measured and analyzed. It sounded like noise, which can be used as the indications of faults in gears. The second method was based on the BLDC-motors vibration characteristics and uses supervised machine learning to separate healthy motors from the faulty ones. Support Vector Machine (SVM) is the suggested machine learning algorithm and 23 different features are used. The best performing model was a Coarse Gaussian SVM, with an overall accuracy of 92.25 % on the validation data.
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Sannellappanavar, Govindraj. "Early Gear Failure Detection in Fatigue Testing of Driveline Components." Thesis, KTH, Maskinkonstruktion (Inst.), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276672.
Full textAbstract:
Early failure detection has been an integral part of condition monitoring of critical systems, such as wind turbines and helicopter rotor drivetrains. An unexplored application of early failure detection is fatigue testing of driveline components. On many occasions, driveline components fail catastrophically, leaving no evidence of the root cause of failure and causing extensive damage to test equipment. This can be prevented by detecting failure in its early stages. Test specimen would be preserved, enabling correlation of test results with design predictions. In this thesis, a method for early failure detection of gear fatigue is proposed. The gears in questions are parts of driveline components undergoing fatigue tests. The proposed method includes generation of an autoregressive model from a healthy, time synchronously averaged vibration signal. The parameters of the generated model are then used to construct a filter, which predicts deviations from the healthy signal. The output of this filter is then processed to detect failure. Vibration data from four run to failure tests were analysed. While the proposed method detected failure in all four data sets, performance was better in tests carried out at high torque and low speed in comparison to tests carried out under low torque and high speeds. Finally, potential improvements in the proposed method to increase its effectiveness are proposed.“Early Failure Detection” (tidig detektion av utmattningsbrott) har länge varit en viktig del av tillståndsövervakning av kritiska system, som till exempel vindkraftverk och drivsystem för rotorblad på helikoptrar. Ett mindre utforskat område av “Early Failure Detection” är utmattningstestning av komponenter för transmissionssystem. Ofta går komponenterna sönder på ett sådant sätt att grundorsaken till haveriet inte går att fastställa, och som riskerar att skada testriggarna. Detta kan förebyggas om haveriet kan upptäckas i ett tidigt skede innan komponenten gar sönder helt och hållet. Testobjeket kan då bevaras, vilket ger möjligheter att korrelera testresultatet till utmattningsberäkningar av konstruktionen. I den här uppsatsen föreslås en metod för Early Failure Detection för drevsatser i växlar. Växlarna ingår i transmissionssystem som utmattningsprovas. Den föreslagna metoden innebär att en autoreggresiv modell skapas från en tids-synkron medelvärdesbildning på den uppmätta signalen för den oförstärda komponenten. Parametrarna från den modellen används sedan för att skapa ett filter som predikterar avvikelser mot den oförstörda komponenten. Slutligen behandlas utsignalen fran det filteret för att upptäcka utmattningsskador pa drevsatsen i växeln. Vibrationsdata fran fyra utmattningsprov har analyserats. I samtliga prov har provet körts tills brott har konstaterats. Utmattningsskador kunde konstateras tidigt, innan brottet inträffade, i tre av de fyra fallen. Slutligen föreslås förslag på utveckling av den använda metoden for att förbättra predikteringarna.
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Milakovic, Stefan. "Utvärdering och användning av maskindata för tillståndsbaserat underhåll i en industriell kontext." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12829.
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Industriellt underhåll har upplevt en utveckling från det ursprungliga akuta avhjälpande underhållet till dagens möjligheter till underhåll baserat på data, så kallat tillståndsbaserat underhåll (CBM). För CBM genomförs endast underhåll vid behov och detta bestäms av aktuell data från den studerade utrustningen. Onödigt underhåll minimeras och antalet plötsliga haverier minskar. Utvecklingen mot Internet of Things (IoT) ger upphov till en stor mängd data som potentiellt kan användas vid CBM-underhåll. En utmaning uppstår dock i att identifiera sådan data och hur denna data kan användas. Denna studie har syftat till att undersöka hur sådan data kan identifieras och hur den kan tänkas användas vid CBM-underhåll. Studien har utförts tillsammans med Quant i Karlskrona där Quant genomför alla underhållsrelaterade aktiviteter åt ABB High Voltage Cables, ett industriföretag som tillverkar högspänningskablar. Arbetet har utgått från tre frågeställningar som har syftat till att: Identifiera datavariabler som kan tänkas ha relevans för CBM-underhåll. Tolka de identifierade datavariablerna för att bedöma hur de kan användas i CBM. Bedöma lämpligheten av en potentiell CBM-implementation baserat på identifierad data jämfört med existerande underhållsmetod. Arbetet har avgränsats genom att fokusera på ett enskilt företag och en enskild fabrik. Utöver detta har en avgränsning gjorts där fokus lagts på några få enskilda maskiner och komponenter. Sekretess har även behövt beaktas vid hantering av känslig information. Studien har huvudsakligen utförts kvalitativt, genom att på djupet fokusera på ett fåtal maskiner och komponenter. Arbetet har genomförts i nära samarbete med några av Quants anställda. Processdata har samlats in direkt från maskinerna och analyserats genom att identifiera och studera avvikelser i data. Intervjuer av olika slag, kompletterade med dokument, har varit en viktig metod för att inhämta information från anställda på Quant, både kring hur data kan tolkas men även kring hur olika processer fungerar. Analytic hierarchy process (AHP) genomfördes i fokusgrupp med anställda för att bedöma lämplig underhållsstrategi. Ett potentiellt tillvägagångssätt har identifierats som tillåter användning av processdata för CBM hos en särskild komponenttyp på företaget. Metoden behöver implementeras och testas men potential finns att minska underhållskostnaderna. Intressanta avvikelser i processdata har identifierats hos en annan komponent som bör studeras vidare för att förstå om processdata och avvikelserna kan användas i en CBM-kontext eller inte. Förbättringsområden hos företaget har identifierats i tillämpningen av vibrationsmätning, vilket är en metod med god potential att användas för CBM-underhåll och därmed minska underhållskostnaderna. Oljeanalys tillämpas redan men en intressant fundering är hur företagets oljefiltrering påverkar möjligheterna att implementera ett prediktivt underhåll i framtiden. Detta är ett område som framtida studier behöver titta på och bedöma hur det ska tacklas. AHP har även bekräftats vara en användbar metod för att bedöma lämpligaste underhållspolicyn.Industrial maintenance has experienced an evolution from the initial corrective maintenance to the possibility of using data based maintenance techniques, so called condition-based maintenance (CBM). Maintenance is only performed when needed under CBM and this is decided based on the data retrieved from the studied equipment. Unnecessary maintenance is minimized and the number of sudden breakdowns decreases. The trend towards Internet of Things (IoT) gives rise to a large amount of data that can potentially be used in CBM maintenance. 'A challenge arises in identifying and using such data. This study has aimed to investigate how such data can be identified and how it might be used in CBM maintenance. This study has been carried out together with Quant in Karlskrona, Sweden, where Quant performs all maintenance related activities for ABB High Voltage Cables, an industrial manufacturing company. The study has been based on three questions that have aimed to: Identify data variables that might be relevant for CBM maintenance. Interpret the identified data variables to assess how they can be used in CBM. Assess the suitability of a potential CBM implementation based on the identified data compared to the existing maintenance method. The study has been delimited by focusing on a single company and a single factory. In addition, a delimitation has been made to focus on a few individual machines and components. A nondisclosure agreement also had to be considered when dealing with sensitive information. This study has mainly been conducted qualitatively, by focusing in-depth on a few machines and components. The work has been done in close collaboration with Quant’s employees. Process data has been collected from the machines and analyzed by identifying and studying data anomalies. Interviews, complemented with documents, has been an important method in obtaining information from Quant employees, both regarding how data can be interpreted but also on how the various processes work. Analytic hierarchy process (AHP) was conducted in a focus group with employees to determine the most appropriate maintenance strategy. One potential approach has been identified that allows the use of process data for CBM on a particular type of component at the company. The method needs to be implemented and tested but the potential exists to reduce maintenance costs. Interesting anomalies in the process data have been identified in another component which should be studied further to understand if the process data and the anomalies can be used in a CBM context or not. Areas for improvement at the company have been identified in the application of vibration measurements, which is a method with good potential to be used in CBM maintenance, thereby reducing maintenance costs. Oil analysis is already used but an interesting question is how the company’s oil filtration affects its ability to implement a predictive maintenance scheme in the future. This is an area that future studies need to look at and assess how it should be tackled. AHP has also been confirmed to be a useful method to determine the most appropriate maintenance policy.
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AZEVEDO, Henrique Dias Machado de. "Um método para Identificação de falhas em componentes e subcomponentes de turbinas eólicas através de monitoramento de Condição baseado em vibração." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16522.
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Desde a década de 1980, a tecnologia de energia eólica sofreu um imenso crescimento em relação tanto ao tamanho da turbina quanto à capacidade instalada a nível mundial. Como a demanda por turbinas eólicas de grande escala e de custo mais baixos de operação e manutenção continua a crescer, o interesse nos sistemas de monitoramento de condição (CMS, do inglês Condition Monitoring System) tem aumentado rapidamente. Os principais componentes (MC, do inglês Main Components) das turbinas eólicas são o foco de praticamente todos CMS já que eles provocam um elevado custo de reparo e tempo de parada. Entretanto, uma grande parte das falhas em MC é causada por danos secundários devido a uma falha de um subcomponente. O objetivo deste trabalho é apresentar e propor um método de análise para identificar defeitos em componentes principais ou subcomponentes de uma turbina eólica. O método de análise proposto se baseia em técnicas de sinais temporais de vibração, nas transformadas rápidas de Fourier e análises envelope obtidas através da transformada de Hilbert. A aplicação do método, em uma turbina eólica instalada em um parque eólico real, permitiu a identificação, com sucesso, de um defeito no rolamento, o qual foi substituído confirmando a falha.
Since the decade of 1980s, wind energy technology has undergone tremendous growth over both turbine size and worldwide installed capacity. As the demand for wind turbines of large-scale and lower cost of operation and maintenance continues to grow, interest in condition monitoring system (CMS) has increased rapidly. The main components (MC) of the wind turbines are the focus of almost all CMS since they cause a high repair cost and downtime. However, a large portion of the MC faults are caused by secondary damages due to a subcomponent failure. The objective of this work is to present and propose a method of analysis to identify defects in major components or subcomponents of a wind turbine. The proposed analysis method is based on time wave analysis, fast Fourier transforms analysis and envelope analysis through Hilbert transforms. The application of the method in a wind turbine installed in a real wind farm, identified a bearing defect successfully, which was replaced confirming the failure.
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Santos, Rodolfo de Sousa [UNESP]. "Detecção de falhas em rolamentos de máquinas rotativas utilizando técnicas de processamentos de sinais." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/151409.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Os sinais de vibrações de máquinas rotativas conduzem a informações dinâmicas da máquina e esta análise é de grande importância no que diz respeito ao monitoramento de condição e diagnósticos de máquinas. Vários métodos de análises têm sido empregados no sentido de diagnosticar falhas em componentes de máquinas tais como engrenagens, rolamentos, dentre outros. Este trabalho apresenta uma análise sobre detecção de falhas em rolamentos de máquinas rotativas, e para esta apreciação utilizou-se os bancos de dados da CASE WESTERN RESERV UNIVERSITY e o banco de dados da FEG/UNESP. O objetivo principal deste trabalho foi a implementação de técnicas avançadas para identificar e caracterizar as falhas que são geradas em rolamentos, vislumbrando o aprimoramento da manutenção baseada na condição. Inicialmente, realizou-se a implementação e simulação no banco de dados da (CWRU), utilizando o software MATLAB e por meio da técnica de ressonância de alta frequência (HFRT), obteve-se resultados satisfatórios, entretanto esta metodologia é limitada uma vez que ela é empregada apenas para regime estacionário. A implementação da técnica HFRT não identificou em alguns casos a frequências para caracterização dos defeitos nas pistas dos rolamentos. Em seguida, utilizou-se a técnica Short Time Fourier Transform-STFT. A implementação proporcionou uma análise bem mais sensível aos impactos gerados nas pistas, pois, com a utilização da STFT, foi possível identificar as frequências características de defeitos. Para efeito de comparação optou-se por utilizar a técnica Wavelet combinada com a técnica do envelope. Esta análise foi aplicada usando a Wavelet Daubechies de ordem 4 (db4), em cuja implementação, realizou-se a decomposição do sinal de um rolamento com defeito e verificou-se qual destes apresentou o maior nível RMS e selecionou-se este sinal, pois o mesmo é o nível ideal para aplicação do método. Realizou-se a mesma apreciação ao banco de dados da FEG/UNESP. A análise realizada da técnica de Wavelet combinada com a técnica HFRT foi a que demonstrou melhor capacidade em relação às técnicas HFRT e STFT. Em seguida realizou-se a implementação da técnica de curtose espectral associada à técnica do envelope foi a que proporcionou os resultados mais precisos e satisfatórios, pois com a aplicação dessa metodologia foi possível a determinação de forma automática da região de ressonância e consequentemente uma melhora na caracterização das frequências de defeitos observadas nos rolamentos dos experimentos realizados em máquinas rotativas.
The vibration signals from rotating machines provide a set of dynamic information, which are very important for continuous condition monitoring of machinery. Several analytical methods have been employed in order to diagnose faults in machines components such as gears, bearings and others. This paper presents a fault detection analysis of rotating machinery bearings, using data from CASE WESTERN UNIVERSITY RESERVOIR and the FEG / UNESP database. The main objective of this work is the implementation of advanced techniques to identify and characterize bearing failures, with the purpose to improve maintenance under working conditions. At first, the implementation and simulation were done with data extracted from the database of (CWRU) using MATLAB software and high-frequency resonance technique (HFRT), which led to satisfactory results. However, this technique is limited since it is used only in a stationary regime. In some cases, the implementation of HFRT technique was not able to identify the defect frequencies of the bearing’s races. Next the STFT Short-Time Fourier Transform technique was used. Its implementation provided a much more sensitive analysis of the impacts on the slopes; using STFT allowed to identify the characteristic defect frequencies. For comparison purposes, the wavelet technique combined with the envelope technique were used. This analysis was applied using Daubechies Wavelet of order 4 (DB4). In its implementation, a defective bearing signal was decomposed into various parts. The signal part with the highest RMS level was selected, because it provides best conditions for applying the method. Analogously, data from the FEG / UNESP database were treated. The Wavelet analysis technique combined with HFRT technique demonstrated better capability with respect to the HFRT and STFT techniques. The implementation of the spectral kurtosis technique associated with the envelope technique provided the most accurate and satisfactory results, since with the application of this methodology it was possible to determine the resonance region automatically. Consequently, this is an improvement regarding the characterization of the defect frequencies of the bearings observed in experiments with rotating machinery.
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Sgotti, Carlos Eduardo. "Investigação do comportamento de defeitos em engrenagens cilíndricas de dentes retos utilizando monitoramento da condição." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/153802.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A falha catastrófica de caixas de engrenagens acarreta em perdas de produção e custos de manutenção. O elemento mecânico que mais falha em uma caixa de engrenagens é o próprio par engrenado. Estas falhas geralmente ocorrem devido a defeitos pontuais nos dentes como desgaste severo e presença de trincas, contrariando os fatores de segurança previamente definidos por normas referentes aos critérios de falhas em engrenagens. O monitoramento da condição do par engrenado busca avaliar parâmetros representativos dos mecanismos de falha do par engrenado. As técnicas de monitoramento da condição mais utilizadas são a análise de vibrações e análise de lubrificantes. Este trabalho realiza uma revisão bibliográfica de técnicas de monitoramento da condição. A parte experimental consiste na avaliação de uma bancada sob três condições: desgaste severo ao longo da vida útil da engrenagem; engrenagem entalhada para simulação de trinca; engrenagem com variação do entalhe para simulação de uma propagação de trinca. A condição da bancada foi avaliada utilizando técnicas de tratamento de sinais de vibração como TSA, sinal residual, demodulação temporal e análise estatística via PDF beta e; técnicas de análise de lubrificantes como contagem de partículas e espectrometrias de raios-x e infravermelho. Todas as técnicas se mostraram adequadas na avaliação da evolução do desgaste excetuando a espectrometria de infravermelho. Apenas as técnicas de vibração se mostraram adequadas para identificar a presença do entalhe. A análise estatística via PDF beta se mostrou útil para identificar a degradação de um dente conforme evolui o tamanho do entalhe.
The catastrophic failure of gearboxes results in production losses and maintenance costs. The mechanical component that most fails in gearboxes are the gears. These failures usually occur before the end of useful life projected by criteria of failure standards due teeth defects as severe wear and cracking. The condition monitoring of gearboxes evaluates parameters which can indicate the mechanism of failure in process in the gear. The most commonly used monitoring techniques of gearboxes are vibration analysis and lubricant analysis. Firstly, this work performs a bibliographic review of condition monitoring techniques. The experimental analysis consists of the evaluation of an experimental workbench under three conditions: severe wear throughout the life of the gear; notched gear for crack simulation and; gear with variation of notch for simulation of a crack propagation. The workbench condition was evaluated using vibration signal treatment techniques such as TSA, Residual Signal, Demodulation, Statistical Moments, Crest Factor and Statistical Analysis using PDF beta and; techniques for analyzing lubricants such as particle counting and x-ray and infrared spectrometry. All the techniques were adequate to evaluate the evolution of wear except infrared spectrometry. Only the vibration techniques were adequate to identify the presence an evolution of the notch. Statistical analysis using PDF beta was useful to identify the degradation of a tooth as the notch size evolved.
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Macintyre, John. "Condition monitoring and neural networks." Thesis, University of Sunderland, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297129.
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Lin, Shui-Town. "Gear condition monitoring by wavelet transform of vibration signals." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318680.
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Rehman, Anees ur. "Vibration-based condition monitoring of a turbomachinery bladed system." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/2557/.
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Danielson, Hugo, and Schmuck Benjamin von. "Robot Condition Monitoring : A first step in Condition Monitoring for robotic applications." Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-66011.
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The industrial world is in constant demand for faster, cheaper and higher quality manufacturing. Robot utilisation and automation has evolved to become a necessary asset to master in order to stay competitive in the global market. With the growing dependency on robots, unexpected downtime and brakedowns can cause devastating loss of revenue. Consequently, this has lead to an increased importance for an accurate condition based way of performing robotic maintenance. As of writing, robots are predominantly maintained through time dependent maintenance. Part replacement is based on statistical models where maintenance is performed without taking the actual robot condition into consideration. As a result an overall level of uncertainty is ensued, where lacking the ability to properly diagnose the robot, also leads to superfluous repairs. Because of the costly impact this has on production, a condition based maintenance approach to robots would yield increased reliability at a lower cost of maintenance. This research focuses on trying to monitor vibrations in a robot, so as to infer about wear and to provide a first step in vibration based Robot Condition Monitoring. This research has been of multidisciplinary nature where robotics, tribology, mechanical component, signal analysis and diagnosis theory have overlapped in several areas throughout the project. The research has provided a vibration baseline and trends of the theoretical bearing defect frequencies for a hypocycloid gearbox installed on an ABB IRB6600 robot. The gearbox was not worn to a level that a severe gearbox degradation was irrefutably detectable and analysable. Accelerometers normally used on wind turbines were used for the project, and are believed to be sufficiently successful in capturing bearing related signals to accredit it for continued use at the preliminary stages of Robot Condition Monitoring development. A worn RV410F hypocycloid gearbox, was dismantled and analysed. Bearings found inside indicate high degrees of moisture corrosion and extensive surface wear. These findings had decisive roles in what future work recommendations where presented. Areas with great potential are condition monitoring through the use of Acoustic Emission and lubrication analysis. Further recommendations include investigating signal analysis techniques such as cepstrum pre-whitening and discrete wavelet transforms.
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Ibrahim, Ghalib Rzayyig. "Design and implementation of gearboxes vibration based condition monitoring system." Thesis, Manchester Metropolitan University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543248.
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The use of gearboxes for power transfer is widespread throughout industry. However, machines today are operating at higher speeds than ever before and gear wear and fatigue failures are serious and legitimate concerns. Incipient fault detection in gears has thus become the subject of intensive investigation and at this stage of development there are many competing condition monitoring methods based on vibration signal analysis. Vibration signals obtained from a gearbox were complex multi-component signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures and a substantial amount of noise. This thesis summarizes the research steps taken after a review of (i) current maintenance strategies, (ii) gearbox condition monitoring techniques, (iii) gear vibration fundamentals and (iv) common gearbox failure modes. A mathematical model of the gearbox was used to predict the effects of load and tooth breakage effects on induced vibrations. A test rig was built around an 11 kW two stage helical gearbox, designed and fabricated for experimental data collection. Simulation and experimental work was carried out for a healthy pair of gears under different loads for different drive speeds and a pair suffering from degrees of tooth breakage. Conventional methods using the time-domain of the vibration signal (RMS, kurtosis, skewness and the zero figure of merit) were used for detecting and diagnose the seeded faults. The total energy method was applied to the gear meshing frequency and its sidebands, as obtained from the FFT, to detect the presence of the faults, and the results compared with those obtained by the conventional techniques. The proposed method appears much more effective at detecting and diagnosing tooth breakage than statistical features extracted from the time-domain. Joint time-frequency domain techniques were then used to determine their effectiveness for diagnosing the seeded faults in the gearbox system when the gearbox operates under output loads and at different speeds. A comparison was made between empirical mode decomposition and smoothed pseudo Wigner-Ville distribution methods based on vibration signature. From the results obtained it appears that the empirical mode decompose technique offers a more effective and faster way to detect faults. To improve signal-to-noise ratio, a novel scheme based on adaptive noise cancellation technique with a least squares algorithms was used on the gearbox experimental vibration signals. It is concluded that this method offers the most effective way of all those tested to detect faults.
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Chen, Mingxian. "Combining the active control of gear vibration with condition monitoring." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286755.
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Andrade, Francisco Arruda Raposo. "New techniques for vibration condition monitoring : Volterra kernel and Kolmogorov-Smirnov." Thesis, Brunel University, 1999. http://bura.brunel.ac.uk/handle/2438/7871.
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This research presents a complete review of signal processing techniques used, today, in vibration based industrial condition monitoring and diagnostics. It also introduces two novel techniques to this field, namely: the Kolmogorov-Smirnov test and Volterra series, which have not yet been applied to vibration based condition monitoring. The first technique, the Kolmogorov-Smirnov test, relies on a statistical comparison of the cumulative probability distribution functions (CDF) from two time series. It must be emphasised that this is not a moment technique, and it uses the whole CDF, in the comparison process. The second tool suggested in this research is the Volterra series. This is a non-linear signal processing technique, which can be used to model a time series. The parameters of this model are used for condition monitoring applications. Finally, this work also presents a comprehensive comparative study between these new methods and the existing techniques. This study is based on results from numerical and experimental applications of each technique here discussed. The concluding remarks include suggestions on how the novel techniques proposed here can be improved.
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Kaewkongka, Tonphong. "Bearing condition monitoring using acoustic emission and vibration : the systems approach." Thesis, Brunel University, 2002. http://bura.brunel.ac.uk/handle/2438/7862.
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This thesis proposes a bearing condition monitoring system using acceleration and acoustic emission (AE) signals. Bearings are perhaps the most omnipresent machine elements and their condition is often critical to the success of an operation or process. Consequently, there is a great need for a timely knowledge of the health status of bearings. Generally, bearing monitoring is the prediction of the component's health or status based on signal detection, processing and classification in order to identify the causes of the problem. As the monitoring system uses both acceleration and acoustic emission signals, it is considered a multi-sensor system. This has the advantage that not only do the two sensors provide increased reliability they also permit a larger range of rotating speeds to be monitored successfully. When more than one sensor is used, if one fails to work properly the other is still able to provide adequate monitoring. Vibration techniques are suitable for higher rotating speeds whilst acoustic emission techniques for low rotating speeds. Vibration techniques investigated in this research concern the use of the continuous wavelet transform (CWT), a joint time- and frequency domain method, This gives a more accurate representation of the vibration phenomenon than either time-domain analysis or frequency- domain analysis. The image processing technique, called binarising, is performed to produce binary image from the CWT transformed image in order to reduce computational time for classification. The back-propagation neural network (BPNN) is used for classification. The AE monitoring techniques investigated can be categorised, based on the features used, into: 1) the traditional AE parameters of energy, event duration and peak amplitude and 2) the statistical parameters estimated from the Weibull distribution of the inter-arrival times of AE events in what is called the STL method. Traditional AE parameters of peak amplitude, energy and event duration are extracted from individual AE events. These events are then ordered, selected and normalised before the selected events are displayed in a three-dimensional Cartesian feature space in terms of the three AE parameters as axes. The fuzzy C-mean clustering technique is used to establish the cluster centres as signatures for different machine conditions. A minimum distance classifier is then used to classify incoming AE events into the different machine conditions. The novel STL method is based on the detection of inter-arrival times of successive AE events. These inter-arrival times follow a Weibull distribution. The method provides two parameters: STL and L63 that are derived from the estimated Weibull parameters of the distribution's shape (y), characteristic life (0) and guaranteed life (to). It is found that STL and 43 are related hyperbolically. In addition, the STL value is found to be sensitive to bearing wear, the load applied to the bearing and the bearing rotating speed. Of the three influencing factors, bearing wear has the strongest influence on STL and L63. For the proposed bearing condition monitoring system to work, the effects of load and speed on STL need to be compensated. These issues are resolved satisfactorily in the project.
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Soltani, Bozchalooi Iman. "Bearing vibration and oil debris signal enhancement for machinery condition monitoring." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27486.
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Vibration signal and lubricant oil condition are two major sources of information for machine health condition monitoring. Though vibration signal is an indirect indicator of machine conditions, it contains very rich information. On the other hand, the lubricating oil analysis provides a direct indicator of machine health conditions. The joint use of the two sources of information would compensate for their limitations and thus better maintenance actions can be expected. However, this alone is not sufficient since the two sources are often severely contaminated by background and machine interference noises. Using such contaminated data without careful de-noising will inevitably cause misleading maintenance decisions and hence premature machine failure as well as lost productivity. As such, this thesis addresses the de-noising issues for both vibration and oil condition signals. Due to different natures of the vibration signals and signals measured through oil debris monitoring sensors, different approaches will be developed in this study for the enhancement of the two types of signals. In de-noising vibration signals, this research focuses on bearings since they are one of the most vulnerable and frequently used components in rotating machinery. The results obtained based on bearings could be applied to other rotating machine components with some modifications.
Wavelet transform, in particular the Gabor wavelet transform, has been used for de-noising impulsive signals measured from faulty bearings. However, it has been a challenging task to select proper wavelet parameters. This work introduces a method to guide the selection process by a smoothness index (SI). The SI is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli of the vibration signal. For the signal contaminated by Gaussian white noise, we have shown that the modulus of the wavelet coefficients follows Rician distribution. Based on this observation, we then prove that the SI converges to a constant number (0.8455...) in the absence of mechanical faults or for very low signal to noise ratio. This result provides a dimensionless SI upper bound corresponding to the most undesirable case. We have also shown that the SI value decreases in the presence of impulses with properly selected parameters.
However, this approach is based on the assumption that the most impulsive components of the measured vibration are due to the faults. This assumption may not be valid in general. On the other hand, the proposed method requires a global search for the minimum SI for all combinations of wavelet parameters in the chosen discretized ranges which is a computationally demanding task. In addition, through bandpass filtering the signal, the in-band noise with frequency content in the range covered by the daughter wavelet is not eliminated. As a result, the performance of the wavelet filter based de-noising method deteriorates as the background noise intensity increases.
To mitigate the above difficulties, a novel scale selection method is proposed. In this approach we incorporated our knowledge of the resonance frequency excitation phenomenon in the scale selection algorithm. Furthermore, to improve the efficiency of the method, spectral subtraction is applied prior to wavelet transform. The proposed spectral subtraction method leads to improvements in both the final result of the process and the capability of the wavelet filter based de-noising method for lower SNR vibration signals. The proposed joint spectral subtraction and wavelet de-noising method has been successfully tested using experimental data.
For the oil condition signals, the main issue is that the oil debris sensor is not only sensitive to the metal debris or particles but the structural vibrations as well. The weak signals of small particles are often concealed in the vibration signals. This either causes false alarm (since the shape of a particle signal resembles that of a vibration signal in certain ways) or leaves existent machine faults undetected. Adaptive Line Enhancement technique is proposed to remove such interferences. The method has been tested on both simulated and experimental data.
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Shen, Chia-Hsuan. "Acoustic Based Condition Monitoring." University of Akron / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=akron1341797408.
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Ou, Qing. "Vibration-based Energy Harvesting for Wireless Sensors used in Machine Condition Monitoring." Thesis, University of Canterbury. Mechanical Engineering, 2012. http://hdl.handle.net/10092/7234.
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In a wide range of industries, machine condition monitoring is one of the most cost effective ways to minimise maintenance efforts and machine downtime. To implement such a system, wireless solutions have increasingly become an attractive proposition due to the ease of installation and minimal infrastructure alternation. However, currently most wireless sensors in the world are powered by a finite battery source. The dependence of batteries not only requires frequent maintenance, but also has adverse environmental consequences associated with battery disposal. These reasons render massive deployment of wireless sensors in the industry problematic. With the advances in semiconductors, power consumption of wireless sensors has been continuously decreasing. It is an inevitable trend for self-powered wireless sensors to emerge and become the norm for machine and environmental monitoring. In this research, vibration is chosen to be the energy source to enable self-powered wireless sensors due to its ubiquitousness in machinery and industrial environments. As a result of relying on resonance, the biggest challenge for vibration-based energy harvesters is their narrow bandwidth. Even a small deviation of the vibration frequency can dramatically reduce the power output. The primary goal of this research is to address this problem. In particular, Piezoelectric generators are identified to be the most suitable technology. In this work, extensive theoretical and experimental studies are conducted in single mass and multi-modal harvesters, and in resonance tuning harvesters by modulus and impedance matching as well as by mechanical actuation. Mathematical modelling plays a significant role in energy harvester designs. A dynamic model that generalises the single degree of freedom models and the continuum models is derived and validated by experiments. The model serves as the building block for the whole research, and it is further refined for the investigation of modulus and impedance matching. In the study of multi-modal harvesters, a continuum model for double-mass piezoelectric cantilever beams is derived and experimentally validated. To study the feasibility of resonance tuning by mechanical means, prototypes were built and performance evaluated. This document details the theoretical basis, concepts and experimental results that extend the current knowledge in the field of energy harvesting. This research work, being highly industrially focused, is believed to be a very significant step forward to a commercial energy harvester that works for a wide range of vibration frequencies.
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Abouhnik, Abdelnasser Abouzid. "An investigation into vibration based techniques for wind turbine blades condition monitoring." Thesis, Manchester Metropolitan University, 2012. http://e-space.mmu.ac.uk/313141/.
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The rapid expansion of wind power has been accompanied by reported reliability problems and the aim is to provide a means of increasing wind turbine reliability, prevent break downs, increase availability and reduce maintenance costs and power outages. This research work reports the development of condition monitoring (CM) for early fault detection in wind turbine blades based on vibration measurements. The research started with a background and a survey of methods used for monitoring wind turbines. Then, finite element modelling (FEM) of three bladed horizontal axis wind turbine (HAWT) was developed to understand the nature and mechanism of the induced vibration. A HAWT test rig was constructed and equipped with computerised vibration measuring system for model verification. Statistical and spectral processing parameters then were used to analyse vibration signals that collected in healthy and faulty cases. Results obtained using time and frequency based techniques are not suitable for extracting blades condition related information. Consequently, empirical mode decomposition method (EMD), principal component analysis method (PCA) and continuous wavelet transform (CWT) are applied for extraction blade condition related features from the measured vibration. The result showed that although these methods generally proved their success in other fields, they have failed to detect small faults or changes in blade structure. Therefore, new techniques were developed using the above mentioned methods combined with feature intensity level (FIL) and crest factor. Namely, those are EDFIL, RMPCA and wavelet based FIL. The new techniques are found to be reliable, robust and sensitive to the severity of faults. Those analysis techniques are suitable to be the detection tool for an integrated wind turbine condition monitoring system. Directions for future work are also given at the end of the thesis.
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Torres, Pérez Eduardo. "Study of vibration severity assessment for Machine Tool spindles within Condition Monitoring." Thesis, KTH, Industriell produktion, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200928.
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Today, machine tools are indispensable for production of manufactured goods. Several industries rely in this equipment to manufacture finished products by removing material through different cutting operations. Automobile, military and aerospace are just examples of industries where machine tools are used intensively. Today these industries strive for higher precision, narrower tolerances and more productivity in order to develop higher quality products using lesser resources and minimizing the impact on the environment.Condition Based Maintenance CBM program has been proved as an effective preventive maintenance strategy to face these challenges. Reduction in downtimes, operation losses and maintenance costs are some of the benefits of adopting a CBM approach. The core of a CBM is Condition Monitoring CM, which refers to the surveillance of a suitable parameter for assessing the need of maintenance tasks in the equipment. These parameters are later compared with reference values to obtain a machine health assessment.In machine tools, vibration level in the spindle units is considered a critical parameter to evaluate machine health during their operational life. This parameter is often associated with bearing damage, imbalance or malfunction of the spindle. Despite the importance of vibration levels there is not ISO standard to evaluated spindle health. This fact obstructs in some extend the planning of maintenance task for these high precision assemblies.In the first part of this work, spindle components are studied and their function explained. Besides the main sources of vibration are listed, putting emphasis in three due to is importance when measuring vibration within condition monitoring of spindles. These are imbalance, bearing damage and critical speed. Later relevant concepts of vibration technology and signal analysis are introduced.In the experimental part of the present study, controlled experiments were carried with the purpose of understanding which factors affect vibration measurements on the spindle housing. Control variables as spindle speed, accelerometer’s angular location, and spindle position were studied. Finally, a contactless excitation device CERS and its potential for industry in detecting bearing damage, is evaluated with two experimental setups.The results indicate that vibration levels measured spindle housing depends on great extent on the angular mounting position of the accelerometers. Results also show that some vibrations severity indicators vary considerably along spindle speed range. It was also found that CERS could be potentially used on condition monitoring of machine tool spindles for detecting onset damage on bearings. However further research is considered necessary for this purpose.Idag är verktygsmaskiner nödvändiga för produktion av tillverkade varor. Flera industrier förlitar på detta utrusning för att tillverka produkter tack vare bortskärning av material i olika bearbetningsoperationer. Bild, militär och flygg-industrin är exempel av industrier där verktygsmaskiner används intensivt. Idag strävar dessa industrier för hög precision, trängre toleranser och mer produktivitet. Allt detta för att utveckla högkvalitet-produkter med mindre resurser och för att minska miljöpåverkan.Tillståndsbaserdat underhåll (Condition Based Maintenance CBM) program har bevisats som en effektiv preventiv underhåll strategi för att bemötta de nämnde utmaningar. Minskning av stopptider, slöseri i produktion och underhålls-relaterade kostnader är flera av fördelar med implementering av CBM synsätt. Kärnan bakom CBM är tillstånd övervakning (Condition Monitoring CM), vilket hänför till bevakning av en lämplig indikator för att bedöma underhållsbehov av maskinen. Dessa parameter jämförs i efterhand med referensvärden för att inhämta en bedömning av maskinens hälsa.I verktygsmaskiner, vibrationnivår i spindlar anses som en kritisk parameter för att utvärdera maskins-hälsa under dess operativt liv. Dessa parameter är ofta sammankopplad med lagerskada, obalans eller funktionsstörningar i spindeln. Trots dess betydelse, vibrationnivår i verktygspidlar är inte reglerad i form av ISO standard. Detta försvårar planeringen av underhåll för spindlarna.I första del av den här arbete, spindels olika komponenter beskrivs och dess funktion förklaras. Dessutom de vibrations huvudsorsaker listas, med fokus på tre viktigaste som är relaterad till tillstånd övervakning of spindlar. Dessa är obalans, lagerskada och kritiska varvtal. Sedan viktiga begrepp inom vibrations mättteknik och analys introduceras.I den experimentella delen av arbetet, kontrolerade tester utfördes i avsikt att förstå vilka faktorer påverkar vibrationsmätningar i spindelhuset. Kontrollvariabler som varvtal, acceloremeter vinkelställning och spindel positon undersöktes. Slutligen, utvärderas ”contactless excitation responses system” CERS samt sitt potential för att upptäcka lagerskador. Detta utfördes med två arrangemang.Resultat indikerar att vibrationsnivåer påverkas i stor utsträckning av accelerometers vinkelställning. Resultat visar också att några vibrations indikatorer varierar betydligt med spindelns varvtal. Det konstaterades också att CERS skulle kunna användas för tillståndsövervakning av verktygspindlars med syfte av upptäcka skador i spindel lager. Däremot mer forskning behövs i detta riktning.
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Aini, Reza. "Vibration monitoring and modelling of shaft/bearing assemblies under concentrated elastohydrodynamic condition." Thesis, Kingston University, 1990. http://eprints.kingston.ac.uk/20759/.
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A five degrees of freedom analysis of a perfect precision grinding spindle supported by a pair of back to back angular contact ball bearings is performed. The ball to race contacts are simulated by a non-linear contact spring, representing the elastic deformation of the mating rolling members. Major frequencies associated with various degrees of freedom are observed and a number of design curves, suggesting the best zones of operation for the simulated spindle under radial/ axial loading are also presented. The gyroscopic contribution of an ideal precision spindle was found to be insignificant. The model was further expanded to study the response characteristics of the spindle under lubricated contact conditions. A regression formula is used to model the non-linear spring/ damper arrangement,corresponding to the contact elastohydrodynamic oil film thickness. It is noted that the presence of the oil film along the line of contacts do not significantly alter the position of the major modes of the system. However, its contribution in damping the amplitude of oscillation are found to be significant. Various graphs indicating the overall system response, subjected to varying oil film viscosity, number of balls and the spindle mass are also presented. Furthermore, experimental investigations are conducted to validate the employed methodology. Good agreement is observed between the results of the simulation and the experimental spectra for the fundamental modes of response. Although manufacturing anamolies are not simulated,the formulated models incorporate sufficient versatility to forsee various spindle/bearing configurations, different loading arrangement as well as various geometrical features of a system to be modelled.
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Paya, Basir Abdul. "Vibration condition monitoring and fault diagnostics of rotating machinery using artificial neural networks." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390220.
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Khan, A. F. "Condition monitoring of rolling element bearings : a comparative study of vibration-based techniques." Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292225.
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