Academic literature on the topic 'Bearing diagnostics'
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Journal articles on the topic "Bearing diagnostics":
1
LEPIARCZYK, Dariusz, and Wacław GAWĘDZKI. "VIBRATION DIAGNOSTIC OF A FRICTION PROCESS IN SLIDE BEARINGS." Tribologia 278, no. 2 (May 1, 2018): 73–80. http://dx.doi.org/10.5604/01.3001.0012.6978.
Abstract:
An analysis of the condition of technical objects is carried out by diagnostic systems, the purpose of which is to detect irregularities in their operation and to prevent damages. In slide bearings, it applies to the diagnostic of friction and thermal phenomena of mating friction pairs. Among many methods of bearing diagnostics, special attention should be paid to vibration diagnostic methods based on measurements of relative vibration parameters or on absolute vibration (displacement, velocity, or acceleration of vibration). Methods of the vibration diagnostic of bearings rely on periodic or continuous measurements of relative vibration parameters of the bearing housing in relation to the rotor (in the case of slide bearings the measurements of the bearing sleeve in relation to the shaft neck) or absolute vibration parameters of the bearing housing (i.e. the sleeve in the case of slide bearing). The article presents a method of vibration diagnostics of friction phenomena that occur during the operation of slide bearings under various lubrication and load conditions. There are presented methods of analysis and the interpretation of measurement data obtained as a result of the conducted slide bearing tests on the laboratory stand. A method for assessing a technical condition of the slide bearing friction pairs is proposed.
2
Veselovska, Nataliya, Serhiy Shargorodskiy, Bohdan Bratslavets, and Olha Yalina. "RESEARCH OF FEATURES OF DEVELOPMENT OF BEARING DEFECTS ON THE BASIS OF WAVELET ANALYSIS." ENGINEERING, ENERGY, TRANSPORT AIC, no. 4(111) (December 18, 2020): 5–13. http://dx.doi.org/10.37128/2520-6168-2020-4-1.
Abstract:
Today the vibrodiagnostic method achieves the highest efficiency and manufacturability for the operation of the technical condition of the technological equipment of the agro-industrial complex. At the same time, this method is one of the most modern methods of technical diagnostics, indicating the kinematic warehouses of diagnostic objects. Vibration analysis is a fundamental tool for diagnostic monitoring of bearings. The vibration signal of defective rolling bearings and its spectrum contain characteristic features by which it is possible to fairly correctly identify the type and location of the defect. At the moment the defective element passes through the loaded zone of the rolling bearing, a pronounced peak, an energy impulse, appears in the vibration. Thus, when a bearing with internal defects is operating, characteristic components appear in vibration - harmonics with natural frequencies, the numerical values of which can be calculated using theoretical formulas using the geometric dimensions of the bearing elements and the rotor speed of the mechanism. In a loaded bearing, four characteristic frequencies can be distinguished that are used for diagnostics - the frequency of the outer bearing cage, the frequency of the inner cage, the cage frequency and the rolling element frequencies. The complexity of the analysis of vibration signals of rolling bearings for the purpose of their diagnostics lies in the fact that the signs of a defective bearing are distributed over a wide range of frequencies, have low vibrational energy and are somewhat random in nature. In addition, the vibration signal is, of course, removed from the body of the equipment containing the bearing, and therefore contains not only information useful from the point of view of bearing diagnostics, but also noise - vibrations produced by other parts of the mechanism. The analysis of methods for diagnosing bearing defects based on wavelet analysis of their vibration signals allows us to single out the most promising direction, which consists in the fact that the bearing vibration signal is decomposed into coefficients using wavelet analysis, after which the most significant coefficients are selected from these coefficients.
3
Sergeev, K. O., and T. P. Volkova. "Study of sensitivity and interference protection of various bearing diagnostics methods." Journal of Physics: Conference Series 2176, no. 1 (June 1, 2022): 012036. http://dx.doi.org/10.1088/1742-6596/2176/1/012036.
Abstract:
Abstract Due to the widespread use of bearing units with rolling bearings in ship equipment, the question of using diagnostics-in-place methods to determine their technical condition is relevant. For diagnostics, various methods of different sensitivity and interference protection can be applied. These qualities are of particular importance for diagnosing the bearing units of ship’s gearboxes access to which can be difficult, and the units themselves include several different types of bearings. The article provides materials on comparing the sensitivity and reliability of two methods for diagnosing rolling bearings: the diagnostic method based on the analysis of the envelope spectrum and diagnostic method based on shock pulses. Measurements were taken by these methods at different points of the bearing unit on the special bench, equipped with two different bearings. Previously, defects on the ring were artificially created on one of the bearings of the unit. Measurements have shown that the shock pulse method provides acceptable results only when a sensor is located directly at the point adjacent to the outer ring of the bearing. At the control points distant from the bearing location, the defect was diagnosed with an error, but the presence of a defect, nevertheless, could be determined by applying monitoring at a specific point of control. The method of analyzing the envelope allows secure detecting and identifying the defect even when a sensor is installed at a considerable distance from the bearing. The influence of the interference generated by the mounted lubrication pump is considered. The envelope spectra and the nature of their changes are provided.
4
Lindstedt, Paweł, and Tomasz Sudakowski. "Prediction the Bearing Reliability on Basis Diagnostic Information." Journal of Konbin 3, no. 1 (January 1, 2007): 27–49. http://dx.doi.org/10.2478/v10040-008-0003-0.
Abstract:
Prediction the Bearing Reliability on Basis Diagnostic Information The new complex method of diagnostics of helicopter's turbine engine Allison 250 in work was. This method be bases on complex diagnostic signals of resulting with simultaneous use three autonomic methods of diagnosing (functional diagnosing, wibroacustic diagnosing and tribologic diagnosing) and of surroundings described with number of crossings of diagnostic sill timbers of these signals. This way of description permits to establish between diagnostic signals quantitative reports and the surroundings and the state of technical object. It the diagnostics was conducted of arrangement bearings two engines Allison 250 built-up on helicopter PZL - KANIA. It was showed, that captured in this way diagnostic signals can be used to prediction of reliability profiles of arrangement bearings.
5
Gaydamaka, Anatoly, Yuri Yuri, Dmytro Borodin, Il'ya Verba, Sergіj Krigіn, and Oleksandr Іshchenko. "DIAGNOSTICS OF ROLLING EQUIPMENT SLIDING BEARINGS." Bulletin of the National Technical University «KhPI» Series: Engineering and CAD, no. 2 (December 30, 2021): 20–26. http://dx.doi.org/10.20998/2079-0775.2021.2.04.
Abstract:
Peculiarities of rolling bearings operation are analyzed. Methods of diagnostics of general purpose sliding bearings and rolling equipment are considered. Direct control of the maximum allowable wear of plain bearings during operation is proposed. It is advisable to provide remote information and consulting services to technical services of metallurgical enterprises for maintenance, repair and operation of crankshaft bearings. Bearings of electric machines of drives of rolls of rolling mills work in extremely difficult conditions with considerable overloads in the environment of the increased vibration. This leads to premature wear of the liner and its fatigue failure. The technical condition of bearings of electric machines of drives of rolls of rolling mills for the purpose of an exception of an emergency stop of production is carried out by indirect control of temperature and vibration. However, these controls do not guarantee an avoidance of an emergency. All problems with working plain bearings can be grouped into three groups: insufficient bearing capacity of the lubricating layer, unregulated clearance between the shaft and the liner, unsatisfactory technical condition of the bearing surfaces. Insufficient bearing capacity of the lubricating layer is more often associated with loss of lubricant properties due to improper maintenance of the lubrication system. The discrepancy between the size of the gap between the shaft and the liner to the normalized value arises from design, technological and operational reasons. Key words: plain bearing; failure; rolling equipment; direct wear control; remote information and consulting services
6
Mironov, Aleksey, Pavel Doronkin, Alexander Priklonskiy, and Sergey Yunusov. "Adaptive Technology Application for Vibration-Based Diagnostics of Roller Bearings on Industrial Plants." Transport and Telecommunication Journal 15, no. 3 (September 1, 2014): 233–42. http://dx.doi.org/10.2478/ttj-2014-0021.
Abstract:
Abstract Roller bearings are widely used in equipment of different applications; therefore, the issues related to the assessment of bearing technical state and localization of bearing faults are quite important and relevant. The reason is that technical state of a bearing is a critical component, which determines efficiency of a mechanism or equipment. For bearings inspection and diagnostics, various methods of vibration-based diagnostics are used. The adaptive technology for vibration-based diagnostics developed in „D un D centrs” is an effective tool for evaluation of technical state of bearings in operation compared to the existing SKF method.
7
SZYCA, MIKOŁAJ. "ANALYSIS OF THE BMA K2400 VERTICAL CENTRIFUGE TURBINE IN TERMS OF BALANCING AND VIBRATION DIAGNOSTICS." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 297, no. 3 (July 2, 2021): 71–80. http://dx.doi.org/10.31891/2307-5732-2021-297-3-71-80.
Abstract:
Physical damage to a material is a diffuse defect in the form of vacancies, microcracks, micro-voids or damaged micro-volumes, which reduce the effective or load-bearing part of the material. Surface fatigue defects, such as deformation and cracks, occur in the bearing during the load transfer. Imbalance is a practical problem in the operation of many rotating machines, causing not only increased vibration of the machine, but also leading to accelerated wear of the rotor bearings. The subject of this work is the analysis of the dynamics of the BMA K2400 centrifuge in terms of the possibility of correcting the balance in the given dynamic state. The paper describes the individual stages of solving the problem of excessive machine vibrations, assuming that its bearings were replaced before the diagnostic test. As a result of the lack of effects after replacing the motor bearings and after analyzing the vibration measurement results presented in article, a decision was made to inspect the centrifuge bearings. The diagnostics was performed again, but it concerned only the bearing node No. 1 with the disassembled basket. The measurements were performed using the DIAMOND 401 AX device, equipped with Wilcoxon 780B acceleration sensors with a sensitivity of 100mV/g. The appearance of a technological defect on the outer ring of the bearing, which is a friction pair with a housing, is not a typical damage for this type of machines and was an interesting problem. The consequence of the occurrence of bearing defects may be an increase in statistical values of the vibration signal and the appearance of new amplitudes in the FFT spectra. A vicious circle is created here, where bearings in poor dynamic condition increase the transmission of vibrations through the machine, and high vibrations accelerate the degradation of the bearings. The poor condition of rolling bearings may also prevent dynamic balancing of the rotor, and thus – lead to further propagation of bearing damage caused by an increased level of the machine’s own vibrations.
8
Widodo, Achmad, I. Haryanto, and T. Prahasto. "Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine." Applied Mechanics and Materials 493 (January 2014): 337–42. http://dx.doi.org/10.4028/www.scientific.net/amm.493.337.
Abstract:
This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.
9
Žegarac, Nikola. "Analysis of influencing factors that can cause errors in the application of modern methods of sliding bearing diagnostics in machine and electrical systems." Vojnotehnicki glasnik 68, no. 4 (2020): 845–76. http://dx.doi.org/10.5937/vojtehg68-27265.
Abstract:
Introduction/purpose: The paper presents the application of modern methods in the diagnostics of sliding bearings and the analysis of influencing factors that can cause errors in such an application. Possibilities to determine with certainty when and where problems affect sliding bearings during system operation are presented. It is also shown how the system will continue to function over time. Causes of failures and the manner of their elimination are predicted, as well as the time for planned maintenance of technical systems. Method: The new method solves the problem of sliding bearing diagnostics by measuring the dynamic trajectories of the sleeve in the sliding bearing and by measuring vibration parameters on the inner and outer surfaces of the technical system. The dynamic trajectories of the bearing sleeve are measured with non-contact probes; therefore, the centering of probes in relation to the geometric center of the bearing is very important. Vibration parameters, directly related to the clearance in the sliding bearing, are measured on the inner and outer surfaces of the system. The choice of vibration parameters and measuring points is very important. This method has a number of advantages over other diagnostic methods, as it is easy to access measuring points. Results: By measuring the dynamic trajectory of the sleeves in the plain bearing and vibration parameters on the inner and outer surfaces, the bearing clearance quantities are determined, including: normal condition, initial clearance size, its further increase, bearing clearance sizes, and the moment when the condition parameters are close to the upper limit of the permissible bearing clearance. Conclusion: New diagnostic methods and monitoring systems can be widely applied to: internal combustion engines, all piston machines, hydroelectric power plants, thermal power plants, processing plants, and many other systems.
10
Gorlov, I., S. Ivanov, V. Knyazkina, and D. Iakupov. "Device for integrated diagnostics of mining machines triboelements." E3S Web of Conferences 326 (2021): 00001. http://dx.doi.org/10.1051/e3sconf/202132600001.
Abstract:
This article presents the results of multiple-conditions wear-resistance tests for the antifriction bearings that are used in a milling tool. The test was performed on a one-fifth milling tool model. The tests were divided into three stages. The first testing stage included tests of a model with a new undamaged bearing. The second stage included tests of a model with a bearing that is worn out by approximately 50%. The third stage included tests of a model with a completely worn out antifriction bearing. Along with vibroacoustic tests, we measured the temperatures of the tested triboelement. In this article, we provide amplitude-frequency values of vibroacoustic signals recorded during the tests of a model with an undamaged antifriction bearing, a bearing with 50% worn-out extent, and 100% worn-out bearing. Appropriate resistance levels of complex machine elements cannot be achieved without systems of triboelements condition control. We suggest attaching temperature and vibroacoustic sensors to the critical elements of a peat winning machine that provide data for integral analysis of triboelemets condition and prevent failures when damage is recorded. Integral analysis of the technical condition of the main triboelements will allow performing the highly precise prognosis of the remaining life of a peat-winning machine, thus preventing failures in peat mining seasons.
Dissertations / Theses on the topic "Bearing diagnostics":
1
Ribadeneira, M. Xavier. "Ball bearing diagnostics with multiple sensors." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/18963.
2
Billington, Scott Alexander. "Sensor and machine condition effects in roller bearing diagnostics." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/17796.
3
Xin, Ge. "Sparse representations in vibration-based rolling element bearing diagnostics." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI051/document.
Abstract:
Bien que le diagnostic des roulements par analyse vibratoire soit un domaine très développé, la recherche sur les représentations parcimonieuses des signaux de vibration est encore nouvelle et difficile pour le diagnostic des machines tournantes. Dans cette thèse, de méthodes nouvelles ont été développées, au moyen de différents modèles stochastiques, associées à des algorithmes efficaces afin de servir l’industrie dans le diagnostic des roulements. Tout d’abord, les modèles parcimonieux présentés dans la littérature sont revus. Les principales publications concernant le diagnostic des machines tournantes ont également été considérées. Enfin, en discutant des avantages et des inconvénients des représentations parcimonieuses, une interprétation des structures creuses d’un point de vue Bayésien est proposée, ce qui donne lieu à deux nouveaux modèles de diagnostic des machines tournantes. Dans un second temps, un nouveau modèle stochastique est proposé : il introduit une variable cachée relative à l’apparition d’impacts et estime le contenu spectral des transitoires correspondants ainsi que le spectre du bruit de fond. Cela donne lieu à un algorithme de détection automatique - sans besoin de pré-filtrage manuel - à partir duquel les fréquences de défaut peuvent être révélées. Le même algorithme permet également de filtrer le signal de défaut de manière très efficace par rapport à d’autres approches basées sur l’hypothèse stationnaire. La performance de l’algorithme est étudiée sur des signaux synthétiques. L’efficacité et la robustesse de la méthode sont également vérifiées sur les signaux de vibration mesurés sur un banc d’essai (engrenages et paliers). Les résultats sont meilleurs ou au moins équivalents à ceux de l’analyse d’enveloppes classique et du kurtogramme rapide. Dans un troisième temps, un nouveau schéma pour l’extraction de signaux cyclostationnaires (CS) est proposé. En considérant la variance périodique en tant que variable cachée, un filtre temporel est conçu de manière à obtenir la reconstruction intégrale des signaux CS caractérisés par une fréquence cyclique préétablie, qui peut être connue à priori ou estimée à partir de la corrélation spectrale. Un intérêt particulier de la méthode est sa robustesse lorsqu’elle est appliquée sur des données expérimentales ainsi qu’une capacité d’extraction supérieure par rapport au filtre de Wiener conventionnel. Finalement, ces exemples expérimentaux témoignent de l’utilisation polyvalente de la méthode à des fins de diagnostic de signaux composés. Pour finir, une analyse comparée utilisant le calcul rapide de la corrélation spectrale est réalisée sur une base de données publiquement disponible et largement utilisée. C’est un point crucial qui fixe un défis non-trivial à résoudreAlthough vibration-based rolling element bearing diagnostics is a very well-developed field, the research on sparse representations of vibration signals is yet new and challenging for machine diagnosis. In this thesis, several novel methods have been developed, by means of different stochastic models, associated with their effective algorithms so as to serve the industry in rolling element bearing diagnostics. First, the sparsity-based model (sparse code, in natural image processing) is investigated based on the current literature. The historical background of sparse representations has been inquired in the field of natural scenes. Along three aspects, its mathematical model with corresponding algorithms has been categorized and presented as a fundamental premise; the main publications are therefore surveyed in the literature on machinery fault diagnosis; finally, an interpretation of sparse structure in the Bayesian viewpoint is proposed which then gives rise to two novel models for machinery fault diagnosis. Second, a new stochastic model is introduced to address this issue: it introduces a hidden variable to indicate the occurrence of the impacts and estimates the spectral content of the corresponding transients together with the spectrum of background noise. This gives rise to an automatic detection algorithm – with no need of manual prefiltering as is the case with the envelope spectrum – from which fault frequencies can be revealed. The same algorithm also makes possible to filter out the fault signal in a very efficient way as compared to other approaches based on the stationary assumption. The performance is investigated on synthetic signals with a high noise-to-signal ratio and also in the case of a mixture of two independent transients. The effectiveness and robustness of the method are also verified on vibration signals measured on a test-bench (gears and bearings). Results are found superior or at least equivalent to those of conventional envelope analysis and fast kurtogram. Third, a novel scheme for extracting cyclostationary (CS) signals is proposed. By regularizing the periodic variance as hidden variables, a time-varying filter is designed so as to achieve the full-band reconstruction of CS signals characterized by some pre-set characteristic frequency. Of particular interest is the robustness on experimental data sets and superior extraction capability over the conventional Wiener filter. It not only deals with the bearing fault at an incipient stage, but it even works for the installation problem and the case of two sources, i.e. bearing and gear faults together. Eventually, these experimental examples evidence its versatile usage on diagnostic analysis of compound signals. Fourth, a benchmark analysis by using the fast computation of the spectral correlation is provided. One crucial point is to move forward the benchmark study of the CWRU data set by uncovering its own unique characteristics
4
Chi, John Nji. "Non-invasive diagnostics of excessive bearing clearance in reciprocating machinery." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/41421.
5
Karimi, Mahdi. "Rolling element bearing fault diagnostics using the blind deconvolution technique." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16432/1/Mahdi_Karimi_Thesis.pdf.
Abstract:
Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failure can be catastrophic and can result in costly downtime. Bearing condition monitoring has thus played an important role in machine maintenance. In condition monitoring, the observed signal at a measurement point is often corrupted by extraneous noise during the transmission process. It is important to detect incipient faults in advance before catastrophic failure occurs. In condition monitoring, the early detection of incipient bearing signal is often made difficult due to its corruption by background vibration (noise). Numerous advanced signal processing techniques have been developed to detect defective bearing signals but with varying degree of success because they require a high Signal to Noise Ratio (SNR), and the fault components need to be larger than the background noise. Vibration analyses in the time and frequency domains are commonly used to detect machinery failure, but these methods require a relatively high SNR. Hence, it is essential to minimize the noise component in the observed signal before post processing is conducted. In this research, detection of failure in rolling element bearing faults by vibration analysis is investigated. The expected time intervals between the impacts of faulty bearing components signals are analysed using the blind deconvolution technique as a feature extraction technique to recover the source signal. Blind deconvolution refers to the process of learning the inverse of an unknown channel and applying it to the observed signal to recover the source signal of a damaged bearing. The estimation time period between the impacts is improved by using the technique and consequently provides a better approach to identify a damaged bearing. The procedure to obtain the optimum inverse equalizer filter is addressed to provide the filter parameters for the blind deconvolution process. The efficiency and robustness of the proposed algorithm is assessed initially using different kinds of corrupting noises. The result show that the proposed algorithm works well with simulated corrupting periodic noises. This research also shows that blind deconvolution behaves as a notch filter to remove the noise components. This research involves the application of blind deconvolution technique with optimum equalizer design for improving the SNR for the detection of damaged rolling element bearings. The filter length of the blind equalizer needs to be adjusted continuously due to different operating conditions, size and structure of the machines. To determine the optimum filter length a simulation test was conducted with a pre-recorded bearing signal (source) and corrupted with varying magnitude noise. From the output, the modified Crest Factor (CF) and Arithmetic Mean (AM) of the recovered signal can be plotted versus the filter length. The optimum filter length can be selected by observation when the plot converges close to the pre-determined source feature value. The filter length is selected based on the CF and AM plots, and these values are stored in a data training set for optimum determination of filter length using neural network. A pre-trained neural network is designed to train the behaviour of the system to target the optimum filter length. The performance of the blind deconvolution technique was assessed based on kurtosis values. The capability of blind deconvolution with optimum filter length developed from the simulation studies was further applied in a life bearing test rig. In this research, life time testing is also conducted to gauge the performance of the blind deconvolution technique in detecting a growing potential failure of a new bearing which is eventually run to failure. Results from unseeded new bearing tests are different, because seeded defects have certain defect characteristic frequencies which can be used to track a specific damaged frequency component. In this test, the test bearing was set to operate continuously until failures occurred. The proposed technique was then applied to monitor the condition of the test bearing and a trend of the bearing life was established. The results revealed the superiority of the technique in identifying the periodic components of the bearing before final break-down of the test bearing. The results show that the proposed technique with optimum filter length does improve the SNR of the deconvolved signal and can be used for automatic feature extraction and fault classification. This technique has potential for use in machine diagnostics.
6
Karimi, Mahdi. "Rolling element bearing fault diagnostics using the blind deconvolution technique." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16432/.
Abstract:
Bearing failure is one of the foremost causes of breakdown in rotating machinery. Such failure can be catastrophic and can result in costly downtime. Bearing condition monitoring has thus played an important role in machine maintenance. In condition monitoring, the observed signal at a measurement point is often corrupted by extraneous noise during the transmission process. It is important to detect incipient faults in advance before catastrophic failure occurs. In condition monitoring, the early detection of incipient bearing signal is often made difficult due to its corruption by background vibration (noise). Numerous advanced signal processing techniques have been developed to detect defective bearing signals but with varying degree of success because they require a high Signal to Noise Ratio (SNR), and the fault components need to be larger than the background noise. Vibration analyses in the time and frequency domains are commonly used to detect machinery failure, but these methods require a relatively high SNR. Hence, it is essential to minimize the noise component in the observed signal before post processing is conducted. In this research, detection of failure in rolling element bearing faults by vibration analysis is investigated. The expected time intervals between the impacts of faulty bearing components signals are analysed using the blind deconvolution technique as a feature extraction technique to recover the source signal. Blind deconvolution refers to the process of learning the inverse of an unknown channel and applying it to the observed signal to recover the source signal of a damaged bearing. The estimation time period between the impacts is improved by using the technique and consequently provides a better approach to identify a damaged bearing. The procedure to obtain the optimum inverse equalizer filter is addressed to provide the filter parameters for the blind deconvolution process. The efficiency and robustness of the proposed algorithm is assessed initially using different kinds of corrupting noises. The result show that the proposed algorithm works well with simulated corrupting periodic noises. This research also shows that blind deconvolution behaves as a notch filter to remove the noise components. This research involves the application of blind deconvolution technique with optimum equalizer design for improving the SNR for the detection of damaged rolling element bearings. The filter length of the blind equalizer needs to be adjusted continuously due to different operating conditions, size and structure of the machines. To determine the optimum filter length a simulation test was conducted with a pre-recorded bearing signal (source) and corrupted with varying magnitude noise. From the output, the modified Crest Factor (CF) and Arithmetic Mean (AM) of the recovered signal can be plotted versus the filter length. The optimum filter length can be selected by observation when the plot converges close to the pre-determined source feature value. The filter length is selected based on the CF and AM plots, and these values are stored in a data training set for optimum determination of filter length using neural network. A pre-trained neural network is designed to train the behaviour of the system to target the optimum filter length. The performance of the blind deconvolution technique was assessed based on kurtosis values. The capability of blind deconvolution with optimum filter length developed from the simulation studies was further applied in a life bearing test rig. In this research, life time testing is also conducted to gauge the performance of the blind deconvolution technique in detecting a growing potential failure of a new bearing which is eventually run to failure. Results from unseeded new bearing tests are different, because seeded defects have certain defect characteristic frequencies which can be used to track a specific damaged frequency component. In this test, the test bearing was set to operate continuously until failures occurred. The proposed technique was then applied to monitor the condition of the test bearing and a trend of the bearing life was established. The results revealed the superiority of the technique in identifying the periodic components of the bearing before final break-down of the test bearing. The results show that the proposed technique with optimum filter length does improve the SNR of the deconvolved signal and can be used for automatic feature extraction and fault classification. This technique has potential for use in machine diagnostics.
7
Abdul-Raheem, Khalid Fatihi. "Automatic bearing fault diagnostics using wavelet analysis and an artificial neural network." Thesis, Glasgow Caledonian University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493933.
Abstract:
Machinery failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. A new technique for an automated detection and diagnosis of rolling bearing conditions is presented in this thesis. The time-domain vibration signals of rolling bearings with different fault condition are pre-processed using Impulse and Laplace wavelet transforms for rolling bearing fault detection and feature extraction, respectively. The wavelet denoising and the wavelet envelope power spectrums are used for bearing fault detection and diagnosis. Furthermore, the extracted features for the wavelet transform coefficients in time and frequency domain are applied as input vectors to Artificial Neural Networks (ANN) for rolling bearing fault classification. The Impulse and Laplace Wavelets shape and the ANN classifier parameters are optimized using a genetic algorithm (GA). To reduce the computation cost, decrease the size, and enhance the reliability of the ANN, only the predominant wavelet transform scales are selected for feature extraction. The results for both real and simulated bearing vibration data show the effectiveness of the proposed technique for bearing condition identification and classification with very high success rate using minimum input features.
8
Shiroishi, Jason William. "Bearing condition diagnostics via multiple sensors using the high frequency resonance technique with adaptive line enhancer." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/17779.
9
Šváb, Štěpán. "Diagnostika stavu malých kuličkových ložisek." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-400673.
Abstract:
The aim of this thesis was to build equipment for testing bearing condition analysis during its life. The theory of bearing parameter and diagnostics methodology for their analysis are discussed. Hardware used for device design and data collection. Finally, the course of measurement and software evaluating the end of the test are discussed.
10
Životský, Petr. "Chybové frekvence ložisek." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-228149.
Abstract:
This dissertation deals with error frequences of bearing because the bearing is part of every rotary machine. Certain type of bearing and caused defect is being compared with error frequences which are acquired by experiment. Various kinds of measurement which are more or less applicable in use are being described. Further the dissertation deals with other possible ways of detection error frequences for particular defects of bearing.
Books on the topic "Bearing diagnostics":
1
Fedorov, Denis, and Aleksandr Maznev. Complexes of technical diagnostics of mechanical equipment of electric rolling stock. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1016342.
Abstract:
The textbook consists of two parts. The first part considers the principles of construction and operation of diagnostic systems to determine the status of parts and assemblies mechanical parts of the electric rolling stock; marked the directions of development of diagnostics systems. The second part is devoted to the physical foundations of the method of acoustic-emission diagnostics of bearing units and diagnostic device — the analyzer of the bearing life of ARP-11 and the application program.
Designed for students majoring in 23.05.03 "Rolling stock of Railways".
2
Wartenberg, Alan A. Providing Integrated Care for Pain and Addiction (DRAFT). Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190265366.003.0005.
Abstract:
The central premise of this chapter on providing integrated care for both pain and addiction is that all patients presenting with pain are at risk for development of substance use disorders. Assessment and treatment of the complex interplay between pain syndromes and substance use disorders proceed most productively by employing an integrated model, with a multidisciplinary approach and with employment of multiple diagnostic instruments. The author describes an integrated care model as it applies to each of the common substances of use: opioids, tobacco, alcohol, benzodiazepines, cannabinoids, barbiturates, and stimulants. The basis for a decision to refer for evaluation or treatment is described. The chapter concludes with an argument for collaboration between disciplines, notably pain medicine and addiction medicine, as being the current standard of acceptable care for patients whose illnesses dwell in both camps. A separate text box provides additional information and resources bearing on this chapter’s topics.
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Poland, Jeffrey, and Serife Tekin, eds. Extraordinary Science and Psychiatry. The MIT Press, 2017. http://dx.doi.org/10.7551/mitpress/9780262035484.001.0001.
Abstract:
The subject of the book is the culture of crisis and controversy that exists in contemporary mental health research, following the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the National Institute of Mental Health’s declaration of it as unfit for guiding research in psychiatry. The book explores both the nature and sources of the crisis as well as whether and, if so, how, it can be overcome. It brings together a collection of original articles that develop and apply various analytical ideas and strategies from the philosophy of science, and from other relevant areas of philosophy and science, with the aim of clarifying some aspects of the current crisis and the associated extraordinary science. The themes of the chapters include understanding the research domain of mental illness, clarifying the nature of the problems that constitute the current crisis, identifying key substantive and methodological assumptions concerning classification and research focused on the domain of mental illness, identifying ideas bearing on how best to respond to the current crisis with respect to the scientific research agenda, and constructively addressing the tension between pursuing a progressive scientific research program concerning mental illness and maintaining a place of prominence for individual persons and their contexts.
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Spencer, Danielle. Metagnosis. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197510766.001.0001.
Abstract:
This book identifies and names the phenomenon of metagnosis: the experience of newly learning in adulthood of a long-standing condition. It can occur when the condition has remained undetected (e.g., colorblindness) and/or when the diagnostic categories themselves have shifted (e.g., ADHD). More broadly, it can occur with unexpected revelations bearing upon selfhood, such as surprising genetic test results. This phenomenon has received relatively scant attention, yet learning of an unknown condition is frequently a significant and bewildering revelation, subverting narrative expectations and customary categories. In addressing the topic this book deploys an evolution of narrative medicine as a robust research methodology comprising interdisciplinarity, narrative attentiveness, and creating a writerly text. Beginning with the author’s own experience of metagnosis, it explores the issues it raises—from communicability to narrative intelligibility to different ways of seeing. Next, it traces the distinctive metagnostic narrative arc through the stages of recognition, subversion, and renegotiation, discussing this trajectory in light of a range of metagnostic experiences, from Blade Runner to real-world midlife diagnoses. Finally, it situates metagnosis in relation to genetic revelations and the broader discourses concerning identity. Proposing that the figure of blindsight—drawn from the author’s metagnostic experience—offers a productive model for negotiating such revelations, the book suggests that better understanding metagnosis will not simply aid those directly affected but will also serve as a bellwether for how we will all navigate advancing biomedical and genomic knowledge, and how we may fruitfully interrogate the very notion of identity.
Book chapters on the topic "Bearing diagnostics":
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Adams, Maurice L. "Bearing Monitoring and Diagnostics." In Bearings, 97–127. Boca Raton : CRC Press, 2017.: CRC Press, 2018. http://dx.doi.org/10.1201/b22177-5.
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Mahgoun, Hafida, and Ridha Ziani. "Bearing Diagnostics Using Time-Frequency Filtering and EEMD." In Applied Condition Monitoring, 44–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96181-1_4.
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Martarelli, Milena, Paolo Chiariotti, and Enrico Primo Tomasini. "Envelope Cepstrum Based Method for Rolling Bearing Diagnostics." In Lecture Notes in Mechanical Engineering, 149–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_12.
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Cotogno, Michele, Marco Cocconcelli, and Riccardo Rubini. "Spatial Acceleration Modulus for Rolling Elements Bearing Diagnostics." In Lecture Notes in Mechanical Engineering, 587–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_51.
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Pennacchi, P., P. Borghesani, S. Chatterton, and R. Ricci. "Bearing Fault Diagnostics Using the Spectral Pattern Recognition." In Springer Proceedings in Physics, 643–48. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2069-5_86.
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Ompusunggu, Agusmian Partogi, Ted Ooijevaar, Bovic Kilundu Y‘Ebondo, and Steven Devos. "Automated Bearing Fault Diagnostics with Cost-Effective Vibration Sensor." In Lecture Notes in Mechanical Engineering, 463–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95711-1_46.
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Zimroz, Radoslaw, Walter Bartelmus, Tomasz Barszcz, and Jacek Urbanek. "Statistical Data Processing for Wind Turbine Generator Bearing Diagnostics." In Condition Monitoring of Machinery in Non-Stationary Operations, 509–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28768-8_52.
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Mauricio, Alexadre, Wade Smith, Junyu Qi, Robert Randall, and Konstantinos Gryllias. "Cyclo-non-stationary Based Bearing Diagnostics of Planetary Gearboxes." In Applied Condition Monitoring, 343–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_35.
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Ramirez, Andrea Sanchez, Richard Loendersloot, Tiedo Tinga, and Giuseppe D’Angelo. "Impact Response Characterization as Basis for Bearing Diagnostics and Prognostics." In Proceedings of the 9th IFToMM International Conference on Rotor Dynamics, 567–76. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-06590-8_46.
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Lemma, Tamiru Alemu, Noraimi Omar, Mebrahitom Asmelash Gebremariam, and Shazaib Ahsan. "Anti-friction Bearing Malfunction Detection and Diagnostics Using Hybrid Approach." In Advances in Material Sciences and Engineering, 117–31. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8297-0_15.
Conference papers on the topic "Bearing diagnostics":
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Novikov, A. A., S. V. Korotkevich, and N. F. Solovey. "SLIDING BEARING DIAGNOSTICS." In BALTTRIB. Aleksandras Stulginskis University, 2017. http://dx.doi.org/10.15544/balttrib.2017.26.
Abstract:
An explanation of electrophysical sounding method using for sliding bearing diagnostics at a boundary friction is given. Electrical circuits and a sliding bearing diagnostic way, where the control analysis of a boundary lubricant layer (BLL) thickness is realized indirectly in accordance with contact resistance parameters are developed. The sliding bearing greasing state is defined according to installed threshold values in advance which achievement defines it operating regime.
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Singh, Kamaljit, Sudhanshu Sharma, and J. P. Sharma. "Antifriction Bearing Sleeves for Diagnostics and Energy Harvesting." In STLE/ASME 2010 International Joint Tribology Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ijtc2010-41152.
Abstract:
Roller ball bearings are the most common and one of the most important components in rotating machinery. Bearings, in general produce vibrations which can be harvested to produce energy and analysis of these vibrations can also be used to determine the condition of ball bearing. In this paper we discuss how to use the bearings for energy harvesting and conditioning monitoring in machines. A sleeve, padded with piezoelectric material, is designed to solve the dual purpose. Piezo electric materials have the ability to generate an electric field or electric potential in response to applied mechanical strain. Tests are conducted on the good and defective bearings to study the effectiveness of the sleeve. Phase fluctuation based processors are found to be effective in ball bearing condition monitoring. For condition monitoring the signature responses for a given time period are studied. At a constant speed of increase in load leads to an increase in voltage generated. For a single non-coated piezo film, voltage varies from 383 mV at 80 lbf to 683 mV at 320 lbf at 40Hz. With the increased stacking of non-coated piezo films at 320 lbf, voltage generated shows an increase of 23 %. Nano-coating mixture (Ferrofluid and Zinc oxide nanoparticles) causes an additional piezoelectric effect on the surface of piezo film as ZnO acts as an additional source of electrons, due to its ability to emit charges at room temperature. The single piezo film configuration at 320 lbf generates a voltage of 663 mV while the voltage increases 2.1 times for a single nano-coated piezo film. Introduction of defects causes increases in the contact stress at the asperities leading to an increase in the vibrations and forces. Also, an increase in vibration and force, leads to an increase in the voltage generated. For a single piezo film configuration, in a normal bearing, the voltage generated is 663 mV while a defective bearing gives a voltage of 698 mV.
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Pozhidaeva, Vyara. "Determining the Roughness of Contact Surfaces of the Rolling Bearings by the Method of Shock Pulses." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-64221.
Abstract:
One of the most effective methods for bearing diagnostics is the method of shock pulses. The fixed pulse levels for specific type of bearings and a specific working position are in corresponds with the parameters, characterizing the resource of the bearing. Roughness is one of those parameters. Adaptation and application of that method to slow-revolutionary large rolling bearings of wheel excavators, spreaders and belt conveyers is commented in the paper. A diagnostic system for control directed towards repair is worked out as a result of the adaptation of standard diagnostic approach for application to non-standard (from a vibrodiagnostics point of view) bearing nodes of mechanization at the bulgarian open-pit mine “Maritza-East Mine” Ltd. In Bulgaria.
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Liu, J., S. Ghafari, W. Wang, F. Golnaraghi, and F. Ismail. "Bearing Fault Diagnostics Based on Reconstructed Features." In 2008 IEEE Industry Applications Society Annual Meeting (IAS). IEEE, 2008. http://dx.doi.org/10.1109/08ias.2008.173.
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Holm-Hansen, Brian T., and Robert X. Gao. "Time-scale analysis adapted for bearing diagnostics." In Photonics East '99, edited by Bhaskaran Gopalakrishnan and San Murugesan. SPIE, 1999. http://dx.doi.org/10.1117/12.359515.
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Borghesani, P., S. Chatterton, P. Pennacchi, and A. Vania. "A Novel Threshold for the Diagnostics of Rolling Element Bearing." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35362.
Abstract:
The identification of the damage type in rolling element bearings is usually performed by means of suitable vibration signal analysis tools such as the most used and simplest method, Envelope Analysis through the corresponding Square Envelope Spectrum. The diagnostics and the monitoring of the bearing health are often performed by means of other approaches based on the evaluation of a damage index as the root mean square, the Kurtosis of the filtered signal, or more efficient indexes as the so-called Ratio of Cyclic Content. At any rate, in the case of real-time diagnostics, the definition of a threshold for the assessment of the bearing health is mandatory due to the presence in the vibration signal of additional sources and noises. In the paper, a threshold for the band-Kurtosis index that depends only on the sampling frequency and the bandwidth of the filter used for the demodulation of the vibration signal has been introduced. The effectiveness of the threshold has been proven by the experimental data of a damaged bearing.
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Bently, Donald E., John W. Grant, and Phillip C. Hanifan. "Active Controlled Hydrostatic Bearings for a New Generation of Machines." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0354.
Abstract:
This paper presents a revolutionary approach of using a fluid-lubricated bearing for both traditional functions (load support, damping, and heat removal) and to actively control the rotor dynamics of rotating machinery. We will discuss how its use in the design of next generation turbomachinery can yield dramatic benefits. This includes an increase in efficiency, operational life, fault diagnostic, and reductions in machine size, weight, and cost. With the use of hydrostatic instead of hydrodynamic lubrication, traditional lubricants can be replaced by fluids more friendly to the process and environment. In this paper a comparison between the new hydrostatic bearing (Bently ServoFluid™ Control Bearing) and active magnetic bearings (AMB) will be presented. The Bently ServoFluid™ Control Bearing is an active controlled externally pressurized (a hybrid hydrostatic) bearing using fluid restoring force to compensate for rotor-related forces. It has the positive features of rolling element, fluid film and magnetic bearings with fewer negative attributes. The fluid restoring forces provide static and dynamic motion control similar to magnetic bearings, but with significantly larger compensation forces and with higher stiffness control. This revolutionary approach enables machinery owners to identify, understand and compensate for rotor system forces, an improvement over simply using vibration (motion) information for machinery diagnostics. This allows more complete diagnostics and prognostics of machine health. The bearing can be used to apply known perturbation forces to the rotor. Perturbation forces enable the determination of rotor system stiffness, and subsequent changes, thus improving machinery diagnostics. It can also provide information, such as the mechanical parameters governing the motion, system linearity, and stability margins for more accurate modeling of machines. Test results will be included to show experimentally determined transfer functions of each of the control loop elements, and predicted rotor forces. A typical root locus plot will be shown demonstrating how the characteristics change with bearing stiffness. Prototype machines, with both low viscosity fluid (water) and typical viscous fluid (T-10 turbine oil), have been built, tested, and successfully operated.
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Chi, John N. "Non-invasive methodology for diagnostics of bearing impacts." In The 14th International Symposium on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, edited by Douglas K. Lindner. SPIE, 2007. http://dx.doi.org/10.1117/12.715856.
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Orsagh, Rolf F., Jeremy Sheldon, and Christopher J. Klenke. "Prognostics/Diagnostics for Gas Turbine Engine Bearings." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38075.
Abstract:
Development of robust in-flight prognostics or diagnostics for oil wetted gas turbine engine components will play a critical role in improving aircraft engine reliability and maintainability. Real-time algorithms for predicting and detecting bearing and gear failures are currently being developed in parallel with emerging flight-capable sensor technologies including in-line oil debris/condition monitors, and vibration analysis MEMS. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data, with probabilistic component models to achieve the best decisions on the overall health of oil-wetted components. By utilizing a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-based estimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Implementation of these oil-wetted component prognostic modules will be illustrated in this paper using bearing and gearbox test stand run-to-failure data.
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Yang, Ling, and Bo Ma. "Bearing Diagnosis of Bogie Gear Box." In 2017 International Conference on Sensing, Diagnostics, Prognostics and Control (SDPC). IEEE, 2017. http://dx.doi.org/10.1109/sdpc.2017.90.
Reports on the topic "Bearing diagnostics":
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Tom, Kwok F. A Primer on Vibrational Ball Bearing Feature Generation for Prognostics and Diagnostics Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2015. http://dx.doi.org/10.21236/ada614145.