Thematische Bibliographien / Condition monitoring of a bearing

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Auswahl der wissenschaftlichen Literatur zum Thema „Condition monitoring of a bearing“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 5. Februar 2022

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Zeitschriftenartikel zum Thema "Condition monitoring of a bearing"

Entezami, Mani, Clive Roberts, Paul Weston, Edward Stewart, Arash Amini, and Mayorkinos Papaelias. "Perspectives on railway axle bearing condition monitoring." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 234, no. 1 (2019): 17–31. http://dx.doi.org/10.1177/0954409719831822.

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Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems and optimised fleet availability. This paper is a review of the state of the art in condition monitoring systems for rolling element bearings, especially the axlebox bearings. This includes exploring the sensing technologies, summarising the main signal processing methods and condition monitoring techniques, i.e. wayside and on-board. Examples of commercially available systems and outputs of current research work are presented. The effectiveness of the current monitoring technologies is assessed and the p– f curve is presented. It is concluded that the research and practical tests on axlebox bearing monitoring are limited compared to the generic bearing applications.

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Lin, Hui Bin, and Kang Ding. "Rolling Element Bearing Condition Monitoring and Diagnosis." Applied Mechanics and Materials 34-35 (October 2010): 332–37. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.332.

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Bearing failure is one of the foremost causes of breakdown in rotating machinery. To date, Envelope detection is always used to identify faults occurring at the Bearing Characteristic Frequencies (BCF). However, because the impact vibration generated by a bearing fault has relatively low energy, it is often overwhelmed by background noise and difficult to identify. Combined the results of extensive experiments performed in a series of bearings with artificial damage, this research investigates the effect of many influencing factors, such as demodulation methods, sampling frequency, variable machine speed and the signals collected in different directions, on the effectiveness of demodulation and the implications for bearing fault detection. By understanding these effects, a more skillful application of the envelope detection in condition monitoring and diagnosis is achieved.

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Gouws, R. "Active magnetic bearing condition monitoring." World Journal of Engineering 10, no. 2 (2013): 179–88. http://dx.doi.org/10.1260/1708-5284.10.2.179.

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GINZINGER, L., M. N. SAHINKAYA, T. SCHINDLER, H. ULBRICH, and P. KEOGH. "4A16 Model-Based Condition Monitoring of an Auxiliary Bearing following Contact Events." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _4A16–1_—_4A16–16_. http://dx.doi.org/10.1299/jsmemovic.2010._4a16-1_.

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Patel, R. K., and V. K. Giri. "Condition monitoring of induction motor bearing based on bearing damage index." Archives of Electrical Engineering 66, no. 1 (2017): 105–19. http://dx.doi.org/10.1515/aee-2017-0008.

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Abstract The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become “transient” in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an ‘index’ representing the health condition of rotating machines.

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Bai, Xiu Qin, Han Liang Xiao, and Lu Zhang. "The Condition Monitoring of Large Slewing Bearing Based on Oil Analysis Method." Key Engineering Materials 474-476 (April 2011): 716–19. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.716.

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Large slewing bearing is a special kind of rolling bearing with heavy load and very low rotation speed. It is important to carry out faults monitoring on this kind of rolling bearing. However, it is difficult to carry out vibration monitoring on such large slewing bearing. The running conditions of slewing bearings of ship loader and stacking crane in Qinghuangdao Port were analyzed using ferrography and spectrometric analysis technology. Monitoring results showed that the slewing bearing of SL-Q1 ship loader was under abnormal wear condition. Further inspection indicated that the rolling elements of this bearing underwent severe wear and broke down. This suggested that it was feasible to evaluating the wear conditions of this type of large low-speed heavy-load rolling bearing using ferrography and spectrometric analysis.

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Sandoval, Diego, Urko Leturiondo, Yolanda Vidal, and Francesc Pozo. "Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis." Sensors 21, no. 3 (2021): 849. http://dx.doi.org/10.3390/s21030849.

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To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

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Li, C. J., J. Ma, and B. Hwang. "Bearing Condition Monitoring by Pattern Recognition Based on Bicoherence Analysis of Vibrations." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 210, no. 3 (1996): 277–85. http://dx.doi.org/10.1243/pime_proc_1996_210_197_02.

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For automatic detection and diagnosis of localized defects in rolling element bearings, bicoherence spectra are used to derive features that signify the condition of a bearing. These features quantitatively describe the degree of phase correlation among any three harmonics of bearing characteristic defect frequencies. Employing these features, a linear discriminant classifier is implemented to detect localized defects on a roller and the outer race of a bearing. Experimental results show that the proposed scheme is effective in bearing defect detection and sensitive to incipient defects.

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James Li, C., and S. Y. Li. "Acoustic emission analysis for bearing condition monitoring." Wear 185, no. 1-2 (1995): 67–74. http://dx.doi.org/10.1016/0043-1648(95)06591-1.

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Weng Zhen, Lim, Anwar P.P Abdul Majeed, Mohd Azraai Mohd Razman, and Ahmad Fakhri Ab. Nasir. "The Condition Based Monitoring for Bearing Health." MEKATRONIKA 2, no. 1 (2020): 63–67. http://dx.doi.org/10.15282/mekatronika.v2i1.6735.

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Bearing is a small component that widely uses in industries, either in rotary machines or shafts. Faulty in bearing might cause massive downtime in the industries, which lead to loss of revenue. This paper intends to find the consequential statistical time-domain-based features that can be used in classification from accelerometry signals for the bearing condition. An accelerometer was used as the data logger device to attain the condition signals from the bearing. Machinery Failure Prevention Technology (MFPT) online dataset has three different bearing conditions: baseline condition, inner faulty condition, and outer faulty condition. Extraction of eight statistical time-domain features was done, which is root-mean-square (RMS), minimum (Min), maximum (Max), mean, median, standard deviation, variance, and skewness. The identification of informative attributes was made using a filter-based method, in which the scoring is done by using the Information gain ratio. For the extracted features, the data splitting of training data to testing data was set to the ratio of 70% and 30%, respectively. The selected feature for classification is then fed into various types of classifiers to observe the effect of this feature selection method on the classification performance. From this research, six features were identified as the significant features: variance, standard deviation, Min, Max, mean, and RMS. It is said that the classification accuracy of the training data and the testing data using the filter-based feature selection method is equivalent to the classification accuracy of all the features selected.

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Dissertationen zum Thema "Condition monitoring of a bearing"

Moussa, Wael. "Thermography-Assisted Bearing Condition Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31379.

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Abstract Despite the large amount of research work in condition based maintenance and condition monitoring methods, there is still a need for more reliable and accurate methods. The clear evidence of that need is the continued dependence on time based maintenance, especially for critical applications such as turbomachinery and airplane engines. The lack of accurate condition monitoring systems could lead to not only the unexpected failures as well as the resulting hazards and repair costs, but also a huge waste of material and time because of unnecessary replacement due to false alarms and unnecessary repair and maintenance. Temperature change is a phenomenon that accompanies every dynamic activity in the universe. However, it has not been adequately exploited for mechanical system condition monitoring. The reason is the slow response of current temperature monitoring systems compared to other condition monitoring methods such as vibration analysis. Many references inferred that the change in temperature is not sensible until approaching the end of the monitored component life and even the whole system life (Kurfess, et al., 2006; Randall, 2011; Patrick, et al., March 7-14, 2009). On the other hand, the most commonly used condition monitoring method, i.e., vibration analysis, is not free from pitfalls. Although vibration analysis has shown success in detecting some bearing faults, for other faults like lubrication problems and gradual wear it is much less effective. Also, it does not give a reliable indication of fault severity for many types of bearing faults. The advancement of thermography as a temperature monitoring tool encourages the reconsideration of temperature monitoring for mechanical system fault detection. In addition to the improved accuracy and responsiveness, it has the advantage of non-contact monitoring which eliminates the need for complex sensor mounting and wiring especially for rotating components. Therefore, in current studies the thermography-based monitoring method is often used either as a distinct method or as a complementary tool to vibration analysis in an integrated condition monitoring system. The main objectives of this study are hence to: 1. Define heat sources in the rolling element bearings and overview two of the most famous bearing temperature calculation methods. 2. Setup a bearing test rig that is equipped with both vibration and temperature monitoring systems. 3. Develop a temperature calculation analytical model for rolling element bearing that include both friction calculation and heat transfer models. The friction calculated by the model will be compared to that calculated using the pre-defined empirical methods. The heat transfer model is used for bearing temperature calculation that will be compared to the experimental measurement using different temperature monitoring devices. 4. Propose a new in-band signal enhancement technique, based on the synchronous averaging technique, Autonomous Time Synchronous Averaging (ATSA) that does not need an angular position measuring device. The proposed method, in addition to the Spectral Kurtosis based band selection, will be used to enhance the bearing envelope analysis. 5. Propose a new method for classification of the bearing faults based on the fault severity and the strength of impulsiveness in vibration signals. It will be used for planning different types of tests using both temperature and vibration methods. 6. Develop and experimentally test a new technique to stimulate the bearing temperature transient condition. The technique is supported by the results of finite element modeling and is used for bearing temperature condition monitoring when the bearing is already running at thermal equilibrium condition.

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Chen, Ping. "Bearing condition monitoring and fault diagnosis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq64993.pdf.

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Gouws, Rupert. "Condition monitoring of active magnetic bearing systems / R. Gouws." Thesis, North-West University, 2007. http://hdl.handle.net/10394/1305.

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Johnston, Andrew Beaton. "Condition monitoring of reciprocating compressors and rolling element bearings." Thesis, University of Aberdeen, 1985. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU365562.

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The prefailure detection of faults in operating plant can effect major rewards in both safety and economy. A successful on-condition maintenance philosophy would pay great dividends particularly in the offshore oil industry where -until recently, only token methods have been employed. Many techniques are available for monitoring mechanical plant and several of these are considered in general terms. Industrial methods are subsequently evaluated on reciprocating compressor and rolling element bearing faults. Bearing fault analysis is considered in two stages. Initially, a series of vibration based techniques are evaluated on a large relatively noise free rotating machine. The techniques of greatest worth carrier spectra, autospectra, time signature analysis and statistical assessments - are then applied to bearings in the hostile environment of a reciprocating machine. It is shown that while discrete faults often produce predictable periodic vibrational patterns, a monitoring system aimed solely at such vibrational phenomena cannot be relied upon. To this end, a diagnostic system must encompass a series of techniques, including carrier spectrum, time signature and statistical analyses. A series of valve and piston faults in reciprocating machines are also studied. By using a number of monitoring techniques, a catalogue of fault characteristics is constructed, and the methods of greatest worth are high-lighted. It is noted that due to the complexities of a reciprocating machine, fault characteristics vary with load, and this must be borne in mind when interpreting the various parameter displays. No single technique can provide a complete cover for all compressor faults, and it is shown that those of greatest worth are acoustic emission, combined pressure and vibration plots, temperature and performance analysis. An indication of compressor temperature and internal cylinder pressure can greatly ease the detection and diagnostic process, and for the latter, bolt load determinations may be a valuable aid.

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Feng, Yanhui. "Novel acoustic emission signal processing methods for bearing condition monitoring." Thesis, University of Leicester, 2008. http://hdl.handle.net/2381/8613.

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Rolling Element Bearing is one of the most common mechanical components to be found in critical industrial rotating machinery. Since the failure of bearings will cause the machine to malfunction and may quickly lead to catastrophic failure of the machinery, it is very important to detect any bearing deterioration at an early stage. In this thesis, novel signal processing methods based on Acoustic Emission measurement are developed for bearing condition monitoring. The effectiveness of the proposed methods is experimentally demonstrated to detect and diagnose localised defects and incipient faults of rolling element bearings on a class of industrial rotating machinery – the iGX dry vacuum pump. Based on the cyclostationary signal model and probability law governing the interval distribution, the thesis proposes a simple signal processing method named LocMax-Interval on Acoustic Emission signals to detect a localised bearing defect. By examining whether the interval distribution is regular, a localised defect can be detected without a priori knowledge of shaft speed and bearing geometry. The Un-decimated Discrete Wavelet Transform denoising is then introduced as a pre-processing approach to improve the effective parameter range and the diagnostic capability of the LocMax-Interval method. The thesis also introduces Wavelet Packet quantifiers as a new tool for bearing fault detection and diagnosis. The quantifiers construct a quantitative time-frequency analysis of Acoustic Emission signals. The Bayesian method is studied to analyse and evaluate the performance of the quantifiers. This quantitative study method is also performed to investigate the relationships between the performance of the quantifiers and the factors which are important in implementation, including the wavelet order, length of signal segment and dimensionality of diagnostic scheme. The results of study provide useful directions for real-time implementation.

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Gómez, Adrián (Gomez ́Velázquez) 1977. "Condition monitoring of bearing damage : test implementation and data acquisition." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/89288.

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Chen, Su Liang. "Development of automated bearing condition monitoring using artificial intelligence techniques." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/195557/.

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A recent series of tapered roller bearing tests have been conducted at the University of Southampton to evaluate the effectiveness of using multiple sensing technologies to detect incipient faults. The test rig was instrumented with on-line sensors including vibration, temperature and electrostatic wear and oil-line debris sensors. Off-line techniques were also used such as debris analysis and bearing surface examination. The electrostatic sensors, in particular, have the potential to detect early decay of tribological contacts within rolling element bearings. These sensors have the unique ability to detect surface charge associated with surface phase transformations, material transfer, tribofilm breakdown and debris generation. Thus, they have the capability to detect contact decay before conventional techniques such as vibration and debris monitoring. However, precursor electrostatic events can not always be clearly seen using time and frequency based techniques. Therefore, an intelligent system that can process signals from multiple sensors is needed to enable early and automatic detection of novel events and provide reasoning to these detected anomalies. Operators could then seek corroborative trends between sensors and set robust alarms to ensure safe running. This has been the motivation of this study.

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Billington, Scott Alexander. "Sensor and machine condition effects in roller bearing diagnostics." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/17796.

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Kaewkongka, Tonphong. "Bearing condition monitoring using acoustic emission and vibration : the systems approach." Thesis, Brunel University, 2002. http://bura.brunel.ac.uk/handle/2438/7862.

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This thesis proposes a bearing condition monitoring system using acceleration and acoustic emission (AE) signals. Bearings are perhaps the most omnipresent machine elements and their condition is often critical to the success of an operation or process. Consequently, there is a great need for a timely knowledge of the health status of bearings. Generally, bearing monitoring is the prediction of the component's health or status based on signal detection, processing and classification in order to identify the causes of the problem. As the monitoring system uses both acceleration and acoustic emission signals, it is considered a multi-sensor system. This has the advantage that not only do the two sensors provide increased reliability they also permit a larger range of rotating speeds to be monitored successfully. When more than one sensor is used, if one fails to work properly the other is still able to provide adequate monitoring. Vibration techniques are suitable for higher rotating speeds whilst acoustic emission techniques for low rotating speeds. Vibration techniques investigated in this research concern the use of the continuous wavelet transform (CWT), a joint time- and frequency domain method, This gives a more accurate representation of the vibration phenomenon than either time-domain analysis or frequency- domain analysis. The image processing technique, called binarising, is performed to produce binary image from the CWT transformed image in order to reduce computational time for classification. The back-propagation neural network (BPNN) is used for classification. The AE monitoring techniques investigated can be categorised, based on the features used, into: 1) the traditional AE parameters of energy, event duration and peak amplitude and 2) the statistical parameters estimated from the Weibull distribution of the inter-arrival times of AE events in what is called the STL method. Traditional AE parameters of peak amplitude, energy and event duration are extracted from individual AE events. These events are then ordered, selected and normalised before the selected events are displayed in a three-dimensional Cartesian feature space in terms of the three AE parameters as axes. The fuzzy C-mean clustering technique is used to establish the cluster centres as signatures for different machine conditions. A minimum distance classifier is then used to classify incoming AE events into the different machine conditions. The novel STL method is based on the detection of inter-arrival times of successive AE events. These inter-arrival times follow a Weibull distribution. The method provides two parameters: STL and L63 that are derived from the estimated Weibull parameters of the distribution's shape (y), characteristic life (0) and guaranteed life (to). It is found that STL and 43 are related hyperbolically. In addition, the STL value is found to be sensitive to bearing wear, the load applied to the bearing and the bearing rotating speed. Of the three influencing factors, bearing wear has the strongest influence on STL and L63. For the proposed bearing condition monitoring system to work, the effects of load and speed on STL need to be compensated. These issues are resolved satisfactorily in the project.

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Nembhard, Adrian. "On-bearing vibration response integration for condition monitoring of rotating machinery." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/onbearing-vibration-response-integration-for-condition-monitoring-of-rotating-machinery(f713f156-11f3-4e10-846e-0b9b709f0ff9).html.

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Vibration-based fault diagnosis (FD) with a simple spectrum can be complex, especially when considering FD of rotating machinery with multiple bearings like a multi-stage turbine. Various studies have sought to better interpret fault spectra, but the process remains equivocal. Consequently, it has been accepted that the simple spectra requires support from additional techniques, such as orbit analysis. But even orbit analysis can be inconclusive. Though promising, attempts at developing viable methods that rival the failure coverage of spectrum analysis without gaining computational complexity remain protracted. Interestingly, few researchers have developed FD methods for transient machine operation, however, these have proven to be involved. Current practices limit vibration data to a single machine, which usually requires a large unique data history. However, if sharing of data between similar machines with different foundations was possible, the need for unique histories would be mitigated. From readily available works, this has not been encountered. Therefore, a simple but robust vibration-based approach is warranted. In light of this, a novel on-bearing vibration response integration approach for condition monitoring of shaft-related faults irrespective of speed and foundation type is proposed in the present study. Vibration data are acquired at different speeds for: a baseline, unbalance, bow, crack, looseness, misalignment, and rub conditions on three laboratory rigs with dynamically different foundations, namely: rigid, flexible support 1 (FS1) and flexible support 2 (FS2). Testing is done on the rigid rig set up first, then FS1, and afterwards FS2. Common vibration features are computed from the measured data to be input to the proposed approach for further processing. First, the proposed approach is developed through its application to a machine at a steady speed in a novel Single-speed FD technique which exploits a single vibration sensor per bearing and fusion of features from different bearings for FD. Initially, vibration features are supplemented with bearing temperature readings with improved classification compared to vibration features alone. However, it is observed that temperature readings are insensitive to faults on the FS1 and FS2 rigs, when compared to vibration features, which are standardised for consistent classification on the different rigs tested. Thus, temperature is not included as a final feature. The observed fault classifications on the different rigs at different speeds with the standardised vibration features are encouraging. Thereafter, a novel Unified Multi-speed FD technique that is based on the initial proposed approach and which works by fusion of vibration features from different bearings at different speeds in a single analysis step for FD is proposed. Experiments on the different rigs repeatedly show the novel Multi-speed technique to be suitable for transient machine operation. Then, a novel generic Multi-foundation Technique (also based on the proposed approach) that allows sharing of vibration data of a wide range of fault conditions between two similarly configured machines with similar speed operation but different foundations is implemented to further mitigate data requirements in the FD process. Observations made with the rigs during steady and transient speed tests show this technique is applicable in situations where data history is available on one machine but lacking on the other. Comparison of experimental results with results obtained from theoretical simulations indicates the approach is consistent. Thus, the proposed approach has the potential for practical considerations.

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Bücher zum Thema "Condition monitoring of a bearing"

Condition Monitoring and Preventative Maintenance Conference (1989 Atlanta, Ga.). Condition Monitoring and Preventative Maintenance proceedings: Presented at the 44th Annual Meeting of the Society of Tribologists and Lubrication Engineers, May 1 - May 4, 1989, Atlanta, Georgia. Society of Tribologists and Lubrication Engineers, 1989.

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Cempel, Czesław. Vibroacoustic condition monitoring. Edited by Haddad S. D. 1939-. Ellis Horwood, 1991.

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Rao, B. K. N. Profitable Condition Monitoring. Springer Netherlands, 1993.

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Rao, B. K. N., ed. Profitable Condition Monitoring. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1616-9.

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Cempel, C. Vibroacoustic condition monitoring. Ellis Horwood, 1991.

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Davies, A., ed. Handbook of Condition Monitoring. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-4924-2.

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Verma, Nishchal K., and Al Salour. Intelligent Condition Based Monitoring. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0512-6.

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Randall, Robert Bond. Vibration-based Condition Monitoring. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470977668.

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Gelman, Len, Nadine Martin, Andrew A. Malcolm, and Chin Kian (Edmund) Liew, eds. Advances in Condition Monitoring and Structural Health Monitoring. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9199-0.

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Tavner, Peter J. Condition monitoring of electrical machines. Research Studies Press, 1987.

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Buchteile zum Thema "Condition monitoring of a bearing"

Hannon, William M. "Rolling Bearing Condition Monitoring." In Encyclopedia of Tribology. Springer US, 2013. http://dx.doi.org/10.1007/978-0-387-92897-5_385.

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Cocconcelli, Marco, Jacopo Cavalaglio Camargo Molano, Riccardo Rubini, Luca Capelli, and Davide Borghi. "Bearing Fault Model for an Independent Cart Conveyor." In Applied Condition Monitoring. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_22.

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Mahgoun, Hafida, and Ridha Ziani. "Bearing Diagnostics Using Time-Frequency Filtering and EEMD." In Applied Condition Monitoring. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96181-1_4.

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Farhat, Mohamed Habib, Xavier Chiementin, Fakher Chaari, Fabrice Bolaers, and Mohamed Haddar. "Time Synchronous Averaging Based Detection of Bearing Defects." In Applied Condition Monitoring. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85584-0_23.

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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. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_35.

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Mabrouk, Abdelileh, Olfa Ksentini, Nabih Feki, Mohamed Slim Abbes, and Mohamed Haddar. "Optimal Linear Quadratic Stabilization of a Magnetic Bearing System." In Applied Condition Monitoring. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76517-0_17.

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Younes, Ramdane, Nouredine Ouelaa, Nacer Hamzaoui, and Abderrazek Djebala. "Experimental Study of Combined Gear and Bearing Faults by Sound Perception." In Applied Condition Monitoring. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41459-1_14.

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Kruczek, Piotr, and Jakub Obuchowski. "Modified Protrugram Method for Damage Detection in Bearing Operating Under Impulsive Load." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51445-1_14.

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Fourati, Aroua, Adeline Bourdon, Didier Rémond, Nabih Feki, Fakher Chaari, and Mohamed Haddar. "Current Signal Analysis of an Induction Machine with a Defective Rolling Bearing." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_5.

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Dybała, Jacek, and Jakub Komoda. "Empirical Signal Decomposition Methods as a Tool of Early Detection of Bearing Fault." In Applied Condition Monitoring. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61927-9_14.

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Konferenzberichte zum Thema "Condition monitoring of a bearing"

Petty, D. J. "Analysis of bearing vibration monitoring." In IEE Colloquium Understanding Your Condition Monitoring. IEE, 1999. http://dx.doi.org/10.1049/ic:19990654.

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Sadoughi, Alireza, and Hamidreza Behbahanifard. "A practical bearing fault diagnoser." In 2008 International Conference on Condition Monitoring and Diagnosis. IEEE, 2008. http://dx.doi.org/10.1109/cmd.2008.4580251.

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Taylor, Barry. "Real-Time Monitoring of Bearing Condition." In ASME 1999 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/99-gt-307.

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Magnetic chip detectors, vibration monitoring devices, and spectrographic oil analysis typically do not detect bearing distress until the bearing is in the latter stages of failure. Now, through the innovation of digital signal processing technology and a breakthrough in inductive sensors, it is possible to provide several months of advance notice on a bearing failure. Unlike magnetic chip detectors, this technology has the capability to track the shedding of both magnetic and nonmagnetic debris from the bearing. It is a true prognostic sensor that detects the first indications of a bearing spall and continues to track, in real-time, the quantities of wear debris being generated by the bearing. The data collected from the sensor can be changed into information such that with a particle trend graph it can clearly be seen when the bearing should be taken out of service prior to a turbine failure and the possibility of expensive secondary damage.

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Gunasekaran, Santhosh, Shrinathan Esakimuthu Pandarakone, Keisuke Asano, Yukio Mizuno, and Hisahide Nakamura. "Condition Monitoring and Diagnosis of Outer Raceway Bearing Fault using Support Vector Machine." In 2018 Condition Monitoring and Diagnosis (CMD). IEEE, 2018. http://dx.doi.org/10.1109/cmd.2018.8535744.

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Snell, O. D., and I. Nairne. "Acoustic bearing monitoring - the future RCM 2008." In 4th IET International Conference on Railway Condition Monitoring (RCM 2008). IEE, 2008. http://dx.doi.org/10.1049/ic:20080340.

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Bouleux, Guillaume, Ali Ibrahim, Francois Guillet, and Remy Boyer. "A subspace-based rejection method for detecting bearing fault in asynchronous motor." In 2008 International Conference on Condition Monitoring and Diagnosis. IEEE, 2008. http://dx.doi.org/10.1109/cmd.2008.4580256.

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Kypuros, Javier A., Constantine M. Tarawneh, Andoni Zagouris, Sean Woods, Brent M. Wilson, and Andrew Martin. "Implementation of Wireless Temperature Sensors for Continuous Condition Monitoring of Railroad Bearings." In ASME 2011 Rail Transportation Division Fall Technical Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/rtdf2011-67017.

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At present there are no existing bearing health monitoring systems capable of continuous monitoring and tracking of railroad bearings on freight cars. Current wayside equipment is used to garner intermittent bearing cup temperatures, which at times could be every 65 km (∼40 mi) or more. Such devices are not designed to provide continuous condition monitoring which would enable users to assess the rate of bearing health degradation and predict when a bearing will require service. To this end, IONX, LLC, a subsidiary of Amsted Rail, Inc., has developed low power Wireless Sensor Nodes (WSNs) which can be retrofitted to existing bearing adapters. The WSNs provide continuous monitoring of bearing temperatures as well as the current ambient temperature. Since the nodes are affixed to the bearing adapter surface, a correlation is necessary to estimate the bearing cup temperature using the measured adapter surface temperature. This paper describes research conducted at The University of Texas-Pan American (UTPA) to devise a reliable mathematical model to correlate both temperatures. Additionally, these wireless nodes are currently in use on ten railroad cars that are part of an Australian fleet. The nodes have been collecting data since March 2010. The acquired data was used to devise and test a series of metrics that can automatically detect distressed bearings and predict time to maintenance. The development of bearing health monitoring metrics and their use to assess bearings in the Australian fleet is also discussed in this paper.

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Ai, Shufeng, Hui Li, and Yuping Zhang. "Condition Monitoring for Bearing Using Envelope Spectrum of EEMD." In 2009 International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2009. http://dx.doi.org/10.1109/icmtma.2009.429.

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Peng Guo. "Wind turbine generator bearing Condition Monitoring with NEST method." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244033.

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Villwock, Sebastian, Henning Zoubek, and Mario Pacas. "Rolling Bearing Condition Monitoring Based on Frequency Response Analysis." In 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. IEEE, 2007. http://dx.doi.org/10.1109/demped.2007.4393067.

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Berichte der Organisationen zum Thema "Condition monitoring of a bearing"

Villaran, M., R. Lofaro, and na. Condition Monitoring of Cables Task 3 Report: Condition Monitoring Techniques for Electric Cables. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1013436.

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Green, Andre Walter. Navy Condition-Based Monitoring Project Updates. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1634947.

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Ulerich, Nancy, Getnet Kidane, Christine Spiegelberg, and Nikolai Tevs. Condition Based Monitoring of Gas Turbine Combustion Components. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1117202.

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Huang, Haiying, Ankur Jain, Jian Luo, Franck Mbanya Tchafa, Jun Yao, and Jiuyuan Nie. Distributed Wireless Antenna Sensors for Boiler Condition Monitoring. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1503678.

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Green, Andre Walter. Navy Condition Based Monitoring Project Update: Requested Materials. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1634945.

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Green, Andre. Navy Condition-Based Monitoring Project Update: Distances Overview. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1657094.

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Green, Andre. Navy Condition-Based Monitoring Project Update: Model Selection. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1679993.

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Sundararajan, Visvanatha. Laboratory Equipment for Machinery Condition Monitoring and Diagnostics. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416046.

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Schimpf, Andrew, L. D. Stephenson, and Ashok Kumar. Condition Monitoring Technology for Civil Works Lock Operating Machinery. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada417254.

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Sheng, Shuangwen. Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1048981.

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