Academic literature on the topic 'Vibration based monitoring and maintenance'
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Journal articles on the topic "Vibration based monitoring and maintenance"
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Senapaty, Goutam, and U. Sathish Rao. "Vibration based condition monitoring of rotating machinery." MATEC Web of Conferences 144 (2018): 01021. http://dx.doi.org/10.1051/matecconf/201814401021.
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This project looks at the different maintenance philosophies and the importance of vibration analysis in predictive maintenance. Since most industries and plants make use of rotational equipment, vibration analysis plays a major role in detecting machine defects and developing flaws before the equipment fails and potentially damages other related equipment and to avoid unwanted breakdowns and downtime. Vibration analysis can help increase the lifetime of equipment when the faults are diagnosed at the right time. Vibration analysis of a rotating table top model is also done to show that some faults might exist even though they are not visible to the naked eye.
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Semma, El Mehdi, Ahmed Mousrij, and Hassan Gziri. "Preliminary study of the vibration-based maintenance implementation: case study." Journal of Quality in Maintenance Engineering 24, no. 2 (May 14, 2018): 134–51. http://dx.doi.org/10.1108/jqme-10-2016-0047.
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Purpose The purpose of this paper is to develop the different phases of the implementation of vibration-based maintenance (VBM). Then, the focus will be on the first two stages, namely, the inventory and feasibility study where each step will be translated into a very detailed implementation process through an industrial case study. Design/methodology/approach The study is based on a state of art on the implementation of the VBM; a survey of national and international experts in the field of VBM and finally an analysis of 30 years of VBM practice in a large Moroccan company in the field of chemical processing, via a collective approach called Diagnostic Court Autonome. Findings The study showed that improving productivity by reducing downtimes due to vibration defects through effective vibration monitoring is possible and investment in equipment and vibration monitoring personnel is largely justified in the company studied. Originality/value This paper presents in detail the two preliminary phases with all procedures describing in a practical way the operating rules to apply and organize the roles of different actors. The work will be useful both to researchers and maintenance managers interested in structuring their vibration monitoring cells.
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Jenab, K., K. Rashidi, and S. Moslehpour. "An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks." International Journal of Enterprise Information Systems 9, no. 4 (October 2013): 43–62. http://dx.doi.org/10.4018/ijeis.2013100104.
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This paper reports a newly developed Condition-Based Maintenance (CBM) model based on Artificial Neural Networks (ANNs) which takes into account a feature (e.g., vibration signals) from a machine to classify the condition into normal or abnormal. The model can reduce equipment downtime, production loss, and maintenance cost based on a change in equipment condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, debris content, and volume of material). The model can effectively determine the maintenance/service time that leads to a low maintenance cost in comparison to other types of maintenance strategy. Neural Networks tool (NNTool) in Matlab is used to apply the model and an illustrative example is discussed.
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Jardine, A. K. S., T. Joseph, and D. Banjevic. "Optimizing condition‐based maintenance decisions for equipment subject to vibration monitoring." Journal of Quality in Maintenance Engineering 5, no. 3 (September 1999): 192–202. http://dx.doi.org/10.1108/13552519910282647.
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Yunusa-kaltungo, Akilu, and Jyoti K. Sinha. "Effective vibration-based condition monitoring (eVCM) of rotating machines." Journal of Quality in Maintenance Engineering 23, no. 3 (August 14, 2017): 279–96. http://dx.doi.org/10.1108/jqme-08-2016-0036.
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Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.
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Koenig, Frank, Pauline Anne Found, and Maneesh Kumar. "Innovative airport 4.0 condition-based maintenance system for baggage handling DCV systems." International Journal of Productivity and Performance Management 68, no. 3 (March 4, 2019): 561–77. http://dx.doi.org/10.1108/ijppm-04-2018-0136.
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PurposeThe purpose of this paper is to present the findings of a recent study conducted with the objective of addressing the problem of failure of baggage carts in the high-speed baggage tunnel at Heathrow Terminal 5 by the development of an innovative condition-based maintenance (CBM) system designed to meet the requirements of 21st century airport systems and Industry 4.0.Design/methodology/approachAn empirical experimental approach to this action research was taken to install a vibration condition monitoring pilot test in the north tunnel at Terminal 5. Vibration data were collected over a 6-month period and analysed to find the threshold of good quality tyres and those with worn bearings that needed replacement. The results were compared with existing measures to demonstrate that vibration monitoring could be used as a predictive model for CBM.FindingsThe findings demonstrated a clear trend of increasing vibration velocity with age and use of the baggage cart wheels caused by wheel mass unbalanced inertia that was transmitted to the tracks as vibration. As a result, preventative maintenance is essential to ensure the smooth running of airport baggage. This research demonstrates that a healthy wheel produces vibration of under 60 mm/s whereas a damaged wheel measures up to 100 mm/s peak to peak velocity and this can be used in real-time condition monitoring to prevent baggage cart failure. It can also run as an autonomous system linked to AI and Industry 4.0 airport logic.Originality/valueWhilst vibration monitoring has been used to measure movement in static structures such as bridges and used in rotating machinery such as railway wheels (Tondon and Choudhury, 1999); this is unique as it is the first time it has been applied on a stationary structure (tracks) carrying high-speed rotating machinery (baggage cart wheels). This technique has been patented and proven in the pilot study and is in the process of being rolled out to all Heathrow terminal connection tunnels. It has implications for all other airports worldwide and, with new economic sensors, to other applications that rely on moving conveyor belts.
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Wang, Jianguo, Minmin Xu, Chao Zhang, Baoshan Huang, and Fengshou Gu. "Online Bearing Clearance Monitoring Based on an Accurate Vibration Analysis." Energies 13, no. 2 (January 13, 2020): 389. http://dx.doi.org/10.3390/en13020389.
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Accurate diagnosis of incipient faults in wind turbine (WT) assets will provide sufficient lead time to apply an optimal maintenance for the expensive WT assets which often are located in a remote and harsh environment and their maintenance usually needs heavy equipment and highly skilled engineers. This paper presents an online bearing clearance monitoring approach to diagnose the change of bearing clearance, providing an early and interpretable indication of bearing health conditions. A novel dynamic load distribution method is developed to efficiently gain the general characteristics of vibration response of bearings without local defects but with small geometric errors. It shows that the ball pass frequency of outer race (BPFO) is the primary exciting source due to biased load distribution relating to bearing clearance. The geometric errors, including various orders of runouts on different bearing parts, can be the secondary excitation source. Both sources lead to compound modulation responses with very low amplitudes, being more than 20 dB lower than that of a small local defect on raceways and often buried by background noise. Then, Modulation Signal Bispectrum (MSB) is identified to purify the noisy signal and Gini index is introduced to represent the peakness of MSB results, thereby an interpretable indicator bounded between 0 and 1 is established to show bearing clearance status. Datasets from both a dedicated bearing test and a run-to-failure gearbox test are employed to verify the performance and reliability of the proposed approach. Results show that the proposed method is capable to indicate a change of about 20 µm in bearing clearance online, which provides a significantly long lead time compared to the diagnosis method that focuses only on local defects. Therefore, this method provides a big opportunity to implement more cost-effective maintenance works or carry out timely remedial actions to prolong the lifespan of bearings. Obviously, it is applicable to not only WT assets, but also most rotating machines.
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Ma, Gang, Ke He Wu, and Pan Pan. "Application of Monitoring Technology Based on Aeolian Vibration in Smart Grid." Advanced Materials Research 341-342 (September 2011): 672–77. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.672.
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The aeolian vibration of overhead transmission lines frequently brings about the breaking fatigue of wires, which makes a serious threat to the security of transmission lines.Today, more and more attentions have been paid to the aeolian vibration both in research and commercial realms . As aeolian vibration monitoring can accurately monitor wire damage, it is conducive to timely maintenance and avoiding accidents. In our paper, we propose a system model adopting bending amplitude method to estimate bending amplitude, calculate dynamic bending strain of wire and obtain early warning tips by comparing with strain chart based on the research of principles of aeolian vibration. Besides, A prototype system is developed to realize the effectiveness of the proposed model.
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Widodo, A., Dj Satrijo, T. Prahasto, and I. Haryanto. "Health State Indicator-Based Vibration Signature for Gearbox Condition Monitoring and Maintenance." IOP Conference Series: Materials Science and Engineering 598 (September 6, 2019): 012073. http://dx.doi.org/10.1088/1757-899x/598/1/012073.
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Daga, Alessandro Paolo, and Luigi Garibaldi. "Machine Vibration Monitoring for Diagnostics through Hypothesis Testing." Information 10, no. 6 (June 7, 2019): 204. http://dx.doi.org/10.3390/info10060204.
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Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher’s Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes.
Dissertations / Theses on the topic "Vibration based monitoring and maintenance"
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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.
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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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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/.
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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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Di, Francesco Gianluca. "Metodologie di Condition Based Maintenance applicata alle macchine idrauliche operatrici." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18242/.
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In questo elaborato di tesi si affronta uno studio della manutenzione su condizione applicata alle macchine idrauliche operatrici, utilizzate per gli impianti di distribuzione idrica e di depurazione delle acque della regione Toscana, gestite dalla società Publiacqua S.p.A.
Nel capitolo 2 viene presentata la tematica della gestione della manutenzione, descrivendo le varie tipologie di manutenzione e le strategie di scelta di queste ultime, per poi passare al concetto di Total Productive Maintenance, ovvero un sistema di gestione della manutenzione che ha come obiettivo l’integrazione tra la produzione e la manutenzione.
Nel capitolo 3 viene presentata la trasformazione digitale del mondo industriale, illustrando i principi generali dell’Industria 4.0 e il suo impatto sulla gestione della manutenzione, introducendo il concetto di Prognostic and Health Management e di manutenzione predittiva.
I capitoli 4 e 5 riguardano le metodologie di manutenzione su condizione adottate per la gestione delle pompe sommerse. Questa tipologia di pompe, ampiamente utilizzate per le acque sotterranee, sono installate a grande profondità, perciò le azioni di manutenzione ordinarie risultano difficoltose e particolarmente costose. A causa di queste problematiche, un approccio efficace risulta essere la manutenzione su condizione. Nello specifico nel capitolo 4 si descrive la metodologia basata sull’analisi dell’assorbimento di corrente, attraverso un algoritmo che analizza l’andamento del valore di assorbimento nel tempo; nel capitolo 5 viene invece illustrata una metodologia basata sull’analisi delle vibrazioni attraverso uno studio effettuato negli impianti di prova e in situ secondo le linee guida dello standard ISO 10816. I risultati delle due metodologie forniscono dei livelli di allarme sullo stato delle pompe, che permettono di poter intervenire per tempo prima di un guasto, fornendo le basi per un passaggio da una manutenzione preventiva ad una su condizione.
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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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Gowid, Samer S. A. A. "Improving the profitability, availability and condition monitoring of FPSO terminals." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/25464.
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The main focus of this study is to improve the profitability, availability and condition monitoring of Liquefied Natural Gas (LNG) Floating Production Storage and Offloading platforms (FPSOs). Propane pre-cooled, mixed refrigerant (C3MR) liquefaction is the key process in the production of LNG on FPSOs. LNG liquefaction system equipment has the highest failure rates among the other FPSO equipment, and thus the highest maintenance cost. Improvements in the profitability, availability and condition monitoring were made in two ways: firstly, by making recommendations for the use of redundancy in order to improve system reliability (and hence availability); and secondly, by developing an effective condition-monitoring algorithm that can be used as part of a condition-based maintenance system. C3MR liquefaction system reliability modelling was undertaken using the time-dependent Markov approach. Four different system options were studied, with varying degrees of redundancy. The results of the reliability analysis indicated that the introduction of a standby liquefaction system could be the best option for liquefaction plants in terms of reliability, availability and profitability; this is because the annual profits of medium-sized FPSOs (3MTPA) were estimated to increase by approximately US$296 million, rising from about US$1,190 million to US$1,485.98 million, if redundancy were implemented. The cost-benefit analysis results were based on the average LNG prices (US$500/ton) in 2013 and 2014. Typically, centrifugal turbines, compressors and blowers are the main items of equipment in LNG liquefaction plants. Because centrifugal equipment tops the FPSO equipment failure list, a Condition Monitoring (CM) system for such equipment was proposed and tested to reduce maintenance and shutdown costs, and also to reduce flaring. The proposed CM system was based on a novel FFT-based segmentation, feature selection and fault identification algorithm. A 20 HP industrial air compressor system with a rotational speed of 15,650 RPM was utilised to experimentally emulate five different typical centrifugal equipment machine conditions in the laboratory; this involved training and testing the proposed algorithm with a total of 105 datasets. The fault diagnosis performance of the algorithm was compared with other methods, namely standard FFT classifiers and Neural Network. A sensitivity analysis was performed in order to determine the effect of the time length and position of the signals on the diagnostic performance of the proposed fault identification algorithm. The algorithm was also checked for its ability to identify machine degradation using datasets for which the algorithm was not trained. Moreover, a characterisation table that prioritises the different fault detection techniques and signal features for the diagnosis of centrifugal equipment faults, was introduced to determine the best fault identification technique and signal feature. The results suggested that the proposed automated feature selection and fault identification algorithm is effective and competitive as it yielded a fault identification performance of 100% in 3.5 seconds only in comparison to 57.2 seconds for NN. The sensitivity analysis showed that the algorithm is robust as its fault identification performance was affected by neither the time length nor the position of signals. The characterisation study demonstrated the effectiveness of the AE spectral feature technique over the fault identification techniques and signal features tested in the course of diagnosing centrifugal equipment faults. Moreover, the algorithm performed well in the identification of machine degradation. In summary, the results of this study indicate that the proposed two-pronged approach has the potential to yield a highly reliable LNG liquefaction system with significantly improved availability and profitability profiles.
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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/.
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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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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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Honc, Lukáš. "Řádová analýza signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-240940.
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This master's thesis deals with order analysis. The first part of the thesis describes common methods for order analysis and methods for processing tacho and vibration signal. The second part contains a brief description of some open source tools for order analysis focusing on Sound and Vibration Measurement Suite (SVMS) package for LabVIEW by National Instruments company. The main purpose of the thesis has been designing and realization of own library for order analysis as a plugin for development tool LabVIEW. In the library, there are implemented methods for order analysis including basic functions for processing vibration and tacho signal. Their brief description with manual for its usage is in the third part of the thesis. In the last chapter, implemented functions are compared with those, which are implemented in SVMS by National Instruments, using both simulated and real data.
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Smit, Wynand Gerhardus. "Fan blade damage detection using on-line vibration monitoring." Diss., Pretoria : [s.n.], 2002. http://upetd.up.ac.za/thesis/available/etd-11302005-091637/.
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Canal, Luiz Antonio. "Proposta de um método para priorização de investimento em monitoramento instrumentado contínuo de equipamentos dinâmicos aplicado em planta petroquímica." Universidade do Vale do Rio dos Sinos, 2017. http://www.repositorio.jesuita.org.br/handle/UNISINOS/6770.
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A busca pela maior segurança industrial em plantas petroquímicas tem sido a pauta principal deste tipo de negócio, já que a possibilidade de acidentes é potencializada pela própria natureza da operação, que envolve inventários inflamáveis, combustíveis ou tóxicos. Muitos dos iniciadores destes eventos advêm de mau funcionamento de seus equipamentos dinâmicos, como motores, bombas e compressores, com causas ligadas a manutenção, operação ou projeto. Neste contexto, é proposto neste trabalho um método para a implantação de melhorias e projetos direcionados para o monitoramento contínuo de equipamentos dinâmicos. O objetivo é de aumentar a segurança industrial e a disponibilidade dos equipamentos, já que o contínuo monitoramento dos equipamentos permite uma melhor supervisão dos mesmos, além de fortalecer a manutenção preventiva baseada em condição. Será apresentada a solução focando no monitoramento de temperatura e vibração em mancais, que normalmente são foco da maior parte das falhas em equipamentos dinâmicos. Inicia-se pela escolha do método para priorização da implantação, baseado em análise multicritério e análise de risco, passando pela proposição e definição dos critérios, culminando com a exemplificação do trabalho em uma planta petroquímica real. Como resultado tem-se uma matriz de risco para apoio na priorização de investimento em monitoramento contínuo, agregando ainda simulações de análise de sensibilidade para a tomada de decisão.
The search for greater industrial safety has been the main guideline in petrochemical business, since the most of accidents are potentiated by the own nature of the operation, which involves flammable, combustible or toxic inventories. Many of the initiators of these events arise from the malfunction of their dynamic equipment, such as motors, pumps and compressors, with causes related to maintenance, operation or design. In this context, it is proposed a method for the implementation of improvements and projects directed to the continuous monitoring of dynamic equipment. The main objective is to increase the industrial safety and the availability, since the continuous monitoring allows a better supervision of the dynamic equipment, besides strengthening the application of condition based maintenance policy. The solution will be presented focusing on the monitoring of equipment temperature and vibration, which indicate of most failures or a situation of non-appropriated operation. This work starts with determining the method to prioritize the implementation, based on multicriteria and risk analysis, explain the criteria and their definition, culminating with the exemplification of the work in a real petrochemical plant. As a result, a risk matrix is presented for support in the prioritization of investment in continuous monitoring, also adding sensitivity analysis simulations for decision making.
Books on the topic "Vibration based monitoring and maintenance"
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Randall, Robert Bond. Vibration-based Condition Monitoring. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470977668.
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Barszcz, Tomasz. Vibration-Based Condition Monitoring of Wind Turbines. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05971-2.
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Guan, Hong. Vibration-based structural health monitoring of highway bridges. La Jolla, CA: Dept. of Structural Engineering, University of California, San Diego, 2008.
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Deraemaeker, Arnaud. New Trends in Vibration Based Structural Health Monitoring. Vienna: Springer Vienna, 2011.
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Deraemaeker, Arnaud, and Keith Worden, eds. New Trends in Vibration Based Structural Health Monitoring. Vienna: Springer Vienna, 2011. http://dx.doi.org/10.1007/978-3-7091-0399-9.
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Guan, Hong. Vibration-based structural health monitoring of highway bridges. La Jolla, CA: Dept. of Structural Engineering, University of California, San Diego, 2008.
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Vibration-based condition monitoring: Industrial, aerospace, and automotive applications. Chichester, West Sussex, U.K: Wiley, 2011.
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Randall, Robert Bond. Vibration-based condition monitoring: Industrial, aerospace, and automotive applications. Chichester, West Sussex, U.K: Wiley, 2010.
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Vlasov, Vladimir, Veniamin Bogumil, and Ali Baytulaev. Digital infrastructure and telematics systems for monitoring the maintenance of roads. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1014643.
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The textbook gives the concept of digital infrastructure, shows its role as the technological basis of modern telematics systems for monitoring the maintenance of roads. The issues of creating and using elements of digital infrastructure in the road sector, as well as technological processes of automated control of maintenance of roads, streets, and highways are considered. The requirements for the main functions and characteristics of the telematics equipment of road vehicles and mechanisms are given.
The material of the manual is based on the results of theoretical research and practical work on the creation and implementation of automated control systems for the maintenance of roads.
Meets the requirements of the federal state educational standards of higher education of the latest generation.
For students studying in technical areas of training and specialties.
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Maznev, Aleksandr, and Oleg Shatnev. Electric apparatus and circuits of rolling stock. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014641.
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Provides information about technical data and design of electrical apparatus of control, monitoring and protection of traction motors of electric rolling stock (EPS), the principles of speed control of locomotives and trains with contactor-resistor and semiconductor converters based on modern element base, a circuit diagram of various types of EPS with manifold and induction motors in modes of traction and braking.
For students of institutions of secondary professional education. It may be useful to students of higher educational institutions, courses of improvement of qualification, the railway workers related to the maintenance and repair of rolling stock.
Book chapters on the topic "Vibration based monitoring and maintenance"
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Courtney, Stuart. "Vibration Based Predictive Maintenance. The Training Requirement." In Profitable Condition Monitoring, 159–63. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1616-9_15.
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Saied, Ramadan O., Mohammed S. Mostafa, and Hussein A. Hussein. "Predictive Maintenance Program Based on Vibration Monitoring." In Design and Modeling of Mechanical Systems - II, 651–60. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17527-0_65.
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Al-Habaibeh, Amin, Ampea Boateng, and Hyunjoo Lee. "Innovative Strategy for Addressing the Challenges of Monitoring Off-Shore Wind Turbines for Condition-Based Maintenance." In Springer Proceedings in Energy, 189–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_24.
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AbstractOff-shore wind energy technology is considered to be one of the most important renewable energy source in the 21st century towards reducing carbon emission and providing the electricity needed to power our cities. However, due to being installed away from the shore, ensuring availability and performing maintenance procedures could be an expensive and time consuming task. Condition Based Maintenance (CBM) could play an important role in enhancing the payback period on investment and avoiding unexpected failures that could reduce the available capacity and increase maintenance costs. Due to being at distance from the shore, it is difficult to transfer high frequency data in real time and because of this data transferring issue, only low frequency-average SCADA data (Supervisory Control And Data Acquisition) is available for condition monitoring. Another problem when monitoring wind energy is the massive variation in weather conditions (e.g. wind speed and direction), which could produce a wide range of operational alerts and warnings. This paper presents a novel case study of integrated event-based wind turbine alerts with time-based sensory data from the SCADA system to perform a condition monitoring strategy to categorise health conditions. The initial results presented in this paper, using vibration levels of the drive train, indicate that the suggested monitoring strategy could be implemented to develop an effective condition monitoring system.
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Mobley, R. Keith. "Condition based maintenance." In Handbook of Condition Monitoring, 35–53. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-4924-2_2.
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Wu, Yulin, Shengcai Li, Shuhong Liu, Hua-Shu Dou, and Zhongdong Qian. "Vibration-Based Condition Monitoring." In Vibration of Hydraulic Machinery, 431–77. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6422-4_11.
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Allenby, G. "Condition based maintenance." In Condition Monitoring and Diagnostic Engineering Management, 155–61. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_25.
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Balshaw, Ryan, P. Stephan Heyns, Daniel N. Wilke, and Stephan Schmidt. "Learning-Based Methods for Vibration-Based Condition Monitoring." In Applied Condition Monitoring, 75–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_8.
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Wang, Lihui, and Xi Vincent Wang. "Condition Monitoring for Predictive Maintenance." In Cloud-Based Cyber-Physical Systems in Manufacturing, 163–92. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67693-7_7.
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Sinha, Jyoti K. "Shaft Torsional Vibration Measurement." In Industrial Approaches in Vibration-Based Condition Monitoring, 209–18. Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9781315147222-10.
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Sinha, Jyoti K. "Simple Vibration Theoretical Concept." In Industrial Approaches in Vibration-Based Condition Monitoring, 11–33. Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9781315147222-2.
Full textConference papers on the topic "Vibration based monitoring and maintenance"
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Rastegari, Ali, Andreas Archenti, and Mohammadsadegh Mobin. "Condition based maintenance of machine tools: Vibration monitoring of spindle units." In 2017 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2017. http://dx.doi.org/10.1109/ram.2017.7889683.
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"Topology Optimisation for High Frequency Vibration Energy Harvesting." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-2.
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Abstract. Topology optimisation has been used to design a piezoelectric energy harvester capable of harvesting the vibration present on a helicopter gearbox. The gearbox vibrations, with frequencies in the kilo-hertz range and having amplitudes of 10-100g (where g = 9.81 m/s2), are generated by gear-meshing within the transmission. These accelerations, large in amplitude and high in frequency, are ideal sources for vibration energy harvesting, with the harvested power potentially used to power autonomous condition-based-maintenance systems. This paper will discuss the first and simplest of the harvesters that were designed and manufactured, i.e. a 0.51 mm thick spring steel cantilever that uses a Pz27 piezoceramic transducer, which is sensitive to 1900 Hz gearbox vibrations and can produce 300 µW from a 2g host acceleration.
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"Vibration-Based Detection of Loosened Bolts on Pipes Attached to Bridges." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-28.
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Abstract. U-bolts are critical points of maintenance in pipeline facilities supported by girders of bridges. However, it is difficult to detect loosened U-bolts by visual inspection. To avoid this problem, we have developed a vibration-based method to detect loosening of bolts based on measurements of actual equipment. First, the vibration of a communication pipeline attached to a road bridge was measured when vehicles passed over it. During the measurements, the U-bolts were set in tightened and loosened states. We found that the frequency changes between the tightened and loosened states was too small for practical use. On the other hand, a strong spectrum in the high frequency region (above 80 Hz) appeared only in the tightened state. Next, a model facility was built to simulate a communication pipeline attached to bridge. A hammering test and modal and frequency response analyses were performed by using the finite element method (FEM). The results suggested that high frequency peaks appearing only in the tightened U-bolt are due to the higher rate of force transfer of the natural frequency from the platform. In addition, the pipe is strongly fixed when the U-bolt is tightened, which may lead to stronger high order modes that cause complex deformation of the pipe. The conventional method is to detect loosening of bolts from changes in the natural frequency. However, in actual equipment, confirming the absence of a high-frequency spectrum is more suitable for detecting loosened bolts.
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Kim, Jung-Yeol, Soon-Wook Kwon, Hyun-Seok Yoo, and Moon-Young Cho. "Development of MEMS-Based Vibration Sensor for Tunnel Construction and Maintenance Monitoring System." In 22nd International Symposium on Automation and Robotics in Construction. International Association for Automation and Robotics in Construction (IAARC), 2005. http://dx.doi.org/10.22260/isarc2005/0021.
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Mauricio, Alexandre, Linghao Zhou, David Mba, and Konstantinos Gryllias. "Vibration Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91150.
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Abstract The core of a helicopter drivetrain is a complex planetary main gearbox (MGB) which reduces the high input speed generated by the engines in order to provide the appropriate torque to the main rotors and to other auxiliary systems. The gearbox consists of various shafts, planetary gears and bearings and operates under varying conditions under excessive friction, heat and high mechanical forces. The components are vulnerable to fatigue defects and therefore Health and Usage Monitoring Systems (HUMS) have been developed in order to monitor the health condition of the gearbox, focusing towards early, accurate and on time fault detection with limited false alarms and missed detections. The main aim of a HUM System is by health monitoring to enhance the helicopters’ operational reliability, to support the maintenance decision making, and to reduce the overall maintenance costs. The importance and the need for more advanced and accurate HUMS have been emphasized recently by the post-accident analysis of the helicopter LN-OJF, which crashed in Norway in 2016. During the last few decades various methodologies and diagnostic indicators/features have been proposed for the monitoring of rotating machinery operating under steady conditions but still there is no global solution for complex structures. A new tool called IESFOgram has been recently proposed by the authors, based on Cyclostationary Analysis, focusing on the accurate selection of a filtering band, under steady and varying speed conditions. Moreover the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper the performance of the tool is tested on a complex planetary gearbox, with several vibration sources. The method is tested, evaluated and compared to state of the art methods on a dataset captured during experimental tests under various operating conditions on a Category A Super Puma SA330 main planetary gearbox, presenting seeded bearing defects of different sizes.
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Morota, Hidetsugu, Toshimitsu Miyazono, and Tatsuro Okumura. "Utilization of CMS (Condition Monitoring System) for CBM (Condition-Based Maintenance)." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75843.
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CBM (Condition-Based Maintenance) is highly effective to make improvement utilization rate of the electric power facility. In case of CBM, it is often the case that vibration diagnosis for rotating components is direct measurement. CMS (Condition Monitoring System), which is tool for the plant performance analysis and diagnose component fault based on heat and mass balance calculations, is beneficial tool for CBM to proceed more effectively. The work is a suggestion of a new approach for CBM using CMS, provides proof-of-concept demonstration of effectiveness of a foreword looking approach to diagnostic and prognostic technology and shows effectiveness of CMS for CBM including application results.
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Mauricio, Alexandre, Junyu Qi, and Konstantinos Gryllias. "Vibration Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76993.
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Wind industry experiences a tremendous growth during the last few decades. As of the end of 2016, the worldwide total installed electricity generation capacity from wind power amounted to 486,790 MW, presenting an increase of 12.5% compared to the previous year. Nowadays wind turbine manufacturers tend to adopt new business models proposing total health monitoring services and solutions, using regular inspections or even embedding sensors and health monitoring systems within each unit. Regularly planned or permanent monitoring ensures a continuous power generation and reduce maintenance costs, prompting specific actions when necessary. The core of wind turbine drivetrain is usually a complicated planetary gearbox. One of the main gearbox components which are commonly responsible for the machinery breakdowns are rolling element bearings. The failure signs of an early bearing damage are usually weak compared to other sources of excitation (e.g. gears). Focusing towards the accurate and early bearing fault detection, a plethora of signal processing methods have been proposed including spectral analysis, synchronous averaging and enveloping. Envelope analysis is based on the extraction of the envelope of the signal, after filtering around a frequency band excited by impacts due to the bearing faults. Kurtogram has been proposed and widely used as an automatic methodology for the selection of the filtering band, being on the other hand sensible in outliers. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. Cyclic Spectral Correlation and Cyclic Spectral Coherence have been presented as powerful tools for condition monitoring of rolling element bearings, exploiting their cyclostationary behaviour. In this work a new diagnostic tool is introduced based on the integration of the Cyclic Spectral Coherence along a frequency band that contains the diagnostic information. A special procedure is proposed in order to automatically select the filtering band, maximizing the corresponding fault indicators. The effectiveness of the methodology is validated using the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking dataset which includes various faults with different levels of diagnostic complexity.
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Dragomirescu, Andrei, Carmen-Anca Safta, Lucian Mandrea, Nicolae Orasanu, and Ioan Magheti. "Preventive Maintenance of Hydraulic Installations Based on Monitoring Cavitation-Induced Vibrations." In 2018 International Conference and Exposition on Electrical And Power Engineering (EPE). IEEE, 2018. http://dx.doi.org/10.1109/icepe.2018.8559752.
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Belegundu, Ashok D., Sumanta K. Nayak, Jacob Loverich, and Michael D. Grissom. "Vibration-Based Damage Accumulation Modeling." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59106.
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A simple methodology is presented which takes input data from wireless accelerometers used in continuous monitoring of large numbers of machines such as pumps, motors, gearboxes, and fans, and outputs a damage accumulation metric which can be used for early warning indication for a broad range of faults. Based on the output, maintenance visits can be made to inspect the machines. Emphasis is on ease of use and broad applicability. It is assumed that the accelerometer data reflects damage occurrence. The approach here is based on time series vibration data analysis that estimates the rate at which damage is accumulated at a given location. This indicator accounts for time-varying symptoms in machines which are often overlooked by traditional vibration diagnostic frequency analysis. As fatigue analysis is the foundation for the damage metric, contribution of repeated load reversal cycles to component damage and the nonlinearity in the relationship between damage and vibration amplitude, are incorporated. A MATLAB code has been developed and validated from simple examples in the literature. The methodology is then applied to a finite element model of a defective shaft-bearing assembly, and in a high pressure pumping field application.
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Jalluri, Chandra, Prashanth Magadi, Mohan Viswanathan, Richard Furness, Werner Kluft, and Friedhelm Hostettler. "An Advanced Vibration Based Real-Time Machine Health and Process Monitoring System." In ASME 2006 International Manufacturing Science and Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/msec2006-21093.
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The ever-increasing emphasis on product quality with increased productivity has been driving the automotive manufacturing industry to find new ways to produce high quality products without increasing production time and manufacturing costs. In addition, automotive manufacturing plants are implementing flexible manufacturing strategies with computer numerical control (CNC) machining centers to address excess capacity, shifting consumer trends and future volume uncertainty of products. Over time, plants have used several preventative and predictive maintenance methods to address machine reliability. Such systems include, but are not limited to, scheduling machine down times at regular intervals to check/replace bearings and other spindle/slide components before they can have an adverse affect on part quality. However, most of these methods and traditional systems are not cost effective and cause significant machine down-times, safety concerns and labor overheads and do not reliably monitor other process issues, such as, clamping, incoming stock variations and thermal phenomena. This paper describes an advanced real-time vibration based machine health and process monitoring system that has been developed to address the above issues. The system, called Condition Indicator Analysis Box for CNC (CIAB™-CNC), is easily configurable, and provides real-time data and historical trends of machines, processes and tooling, enabling manufacturing plants to make accurate predictions regarding future production runs. The system also aids in the optimization of preventative maintenance tasks in a cost effective manner. The developed system monitors machine spindle and slide for unbalance, misalignment, damaged/spalled bearings, mechanical looseness, and ball screw issues. Additionally, it performs in-process monitoring during machining as well as non-machining by individual tool and/or feature to detect tool breakages, quality issues and other gross process or machine anomalies. Innovative statistical trending algorithms enable the system to automatically adapt to valid process/parameter changes and significantly reduce the chances of false alarms and warnings. The developed system provides manufacturing plants with a tool to analyze machine tools and their associated components in an effort to gather information they can use effectively to make decisions regarding flexible machines, processes and tooling.
Reports on the topic "Vibration based monitoring and maintenance"
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Miele, Sarah, Yanqing Bao, Pranav Karve, Sankaran Mahadevan, Vivek Agarwal, Eric Giannini, and Jinying Zhu. Vibration-Based Techniques for Concrete Structural Health Monitoring. Light Water Reactor Sustainability Program report. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1546748.
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Yacout, A. M., and Y. Orechwa. Material degradation analysis and maintenance decisions based on material condition monitoring during in-service inspections. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/380364.
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Piercy, Candice, Safra Altman, Todd Swannack, Carra Carrillo, Emily Russ, and John Winkelman. Expert elicitation workshop for planning wetland and reef natural and nature-based features (NNBF) futures. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41665.
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This special report discusses the outcomes of a September 2019 workshop intended to identify barriers to the consideration and implementation of natural and nature-based features (NNBF) in US Army Corps of Engineers (USACE) civil works projects. A total of 23 participants representing seven USACE districts, the US Army Engineer Research and Development Center (ERDC), and the University of California–Santa Cruz met at USACE’s South Atlantic Division Headquarters in Atlanta, Georgia, to discuss how to facilitate the implementation of NNBF into USACE project planning for wetlands and reefs using six categories: (1) site characterization, (2) engineering and design analysis, (3) life-cycle analysis, (4) economic analysis, (5) construction analysis, (6) and operation and maintenance (and monitoring). The workshop identified seven future directions in wetland and reef NNBF research and development: • Synthesize existing literature and analysis of existing projects to better define failure modes. • Determine trigger points that lead to loss of feature function. • Identify performance factors with respect to coastal storm risk management (CSRM) performance as well as ecological performance. • Focus additional research into cobenefits of NNBF. • Quantify the economic life-cycle costs of a project. • Improve technology transfer with regards to NNBF research and topics.
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Mazari, Mehran, Siavash F. Aval, Siddharth M. Satani, David Corona, and Joshua Garrido. Developing Guidelines for Assessing the Effectiveness of Intelligent Compaction Technology. Mineta Transportation Institute, January 2021. http://dx.doi.org/10.31979/mti.2021.1923.
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Many factors affect pavement compaction quality, which can vary. Such variability may result in an additional number of passes required, extended working hours, higher energy consumption, and negative environmental impacts. The use of Intelligent Compaction (IC) technology during construction can improve the quality and longevity of pavement structures while reducing risk for contractors and project owners alike. This study develops guidelines for the implementation of IC in the compaction of pavement layers as well as performing a preliminary life-cycle cost analysis (LCCA) of IC technology compared to the conventional compaction approach. The environmental impacts of the improved construction process were quantified based on limited data available from the case studies. The LCCA performed in this study consisted of different scenarios in which the number of operating hours was evaluated to estimate the cost efficiency of the intelligent compaction technique during construction. The analyses showed a reduction in energy consumption and the production of greenhouse gas (GHG) emissions with the use of intelligent compaction. The LCCA showed that the use of IC technology may reduce the construction and maintenance costs in addition to enhancing the quality control and quality assurance (QC/QA) process. However, a more comprehensive analysis is required to fully quantify the benefits and establish more accurate performance indicators. A draft version of the preliminary guidelines for implementation of IC technology and long-term monitoring of the performance of pavement layers compacted thereby is also included in this report.