Zeitschriftenartikel: „Condition monitoring of a bearing“

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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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4

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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5

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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Hariharan, V., and P. S. S. Srinivasan. "Condition monitoring studies on ball bearings considering solid contaminants in the lubricant." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 8 (2010): 1727–48. http://dx.doi.org/10.1243/09544062jmes1885.

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Rolling element bearings are common in any rotating machinery. They are subject to failure under continuous running. Therefore they have received a great deal of attention in the field of condition monitoring. In rolling element bearings, contamination of lubricant grease by solid particles is one of the several reasons for an early bearing failure. In this context, this article investigates the effect of contamination of lubricant by solid particles on the dynamic behaviour of rolling bearings. Silica powder at three concentration levels and different particle sizes was used to contaminate the lubricant. Experimental tests have been performed on the ball bearings lubricated with grease, and the trends in the amount of vibration affected by the contamination of the grease were determined. The contaminant concentration as well as the particle size is varied. Vibration signatures were analysed in terms of root mean square (RMS) values. From the results, some fruitful conclusions are made about the bearing performance. The effects of contaminant and the bearing vibration are studied for both good and defective bearings. The results show significant variation in the RMS velocity values on varying the contaminant concentration and particle size.

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Jamaludin, N., D. Mba, and R. H. Bannister. "Condition monitoring of slow-speed rolling element bearings using stress waves." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 215, no. 4 (2001): 245–71. http://dx.doi.org/10.1177/095440890121500401.

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Condition monitoring of rolling element bearings through the use of vibration analysis is an established technique for detecting early stages of component degradation. However, this success is not mirrored at rotational speeds below 16r/min. At such speeds the energy generated from bearing defects might not show as an obvious change in signature and thus becomes undetectable using conventional vibration measuring equipment. This paper presents an investigation into the applicability of stress wave analysis for detecting early stages of bearing damage at a rotational speed of 1.12r/min (0.0187 Hz). Furthermore, it reviews work undertaken in monitoring bearings rotating at speeds below 16r/min.

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Liu, Rende. "Condition monitoring of low‐speed and heavily loaded rolling element bearing." Industrial Lubrication and Tribology 59, no. 6 (2007): 297–300. http://dx.doi.org/10.1108/00368790710820892.

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PurposeThis paper sets out to develop a reliable analysis method based upon a low‐cost procedure to monitor the wear condition of low‐speed and heavily loaded rolling element bearing.Design/methodology/approachSpecial solvents for grease are invented and new test methods, including spectroscopy and ferrography of used grease, are developed to monitor the wear condition of a deferred bearing of ladle turret in continuous casting.FindingsAccording to the analytical results, the service life of the ladle turret bearing in No. 1 continuous casting machine is extended to 14 years and significant expense is saved, which proved that it is feasible for grease analysis to be used in the condition monitoring of low speed and heavily loaded rolling element bearing, especially those deferrable bearings.Research limitations/implicationsThe fault mechanism of the huge bearing is not estimated.Practical implicationsOne useful analysis method to monitor the wear condition of low speed and heavily loaded rolling element bearing is reported, and it can be used in other industrial fields.Originality/valueThis paper provides a way of studying condition monitoring of low‐speed and heavily loaded rolling element bearings.

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Lei, Lei, Dongli Song, Zhendong Liu, Xiao Xu, and Zejun Zheng. "Displacement Identification by Computer Vision for Condition Monitoring of Rail Vehicle Bearings." Sensors 21, no. 6 (2021): 2100. http://dx.doi.org/10.3390/s21062100.

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Bearings of rail vehicles bear various dynamic forces. Any fault of the bearing seriously threatens running safety. For fault diagnosis, vibration and temperature measured from the bogie and acoustic signals measured from trackside are often used. However, installing additional sensing devices on the bogie increases manufacturing cost while trackside monitoring is susceptible to ambient noise. For other application, structural displacement based on computer vision is widely applied for deflection measurement and damage identification of bridges. This article proposes to monitor the health condition of the rail vehicle bearings by detecting the displacement of bolts on the end cap of the bearing box. This study is performed based on an experimental platform of bearing systems. The displacement is monitored by computer vision, which can image real-time displacement of the bolts. The health condition of bearings is reflected by the amplitude of the detected displacement by phase correlation method which is separately studied by simulation. To improve the calculation rate, the computer vision only locally focuses on three bolts rather than the whole image. The displacement amplitudes of the bearing system in the vertical direction are derived by comparing the correlations of the image’s gray-level co-occurrence matrix (GLCM). For verification, the measured displacement is checked against the measurement from laser displacement sensors, which shows that the displacement accuracy is 0.05 mm while improving calculation rate by 68%. This study also found that the displacement of the bearing system increases with the increase in rotational speed while decreasing with static load.

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Wan, Li Rong, Guang Yu Zhou, Cheng Long Wang, and Wen Ming Zhao. "Mine Hoist Bearing Condition Monitoring and Fault Diagnosis System Based on Labview." Advanced Materials Research 317-319 (August 2011): 1232–36. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1232.

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By taking full advantage of the technologies of data acquisition, signal analysis and processing and fault diagnosis, this thesis carries out a research on the realization method of mine hoist bearing condition monitoring and fault diagnosis. Firstly, this thesis takes a technical analysis for rolling bearing. Secondly, based on determining the overall framework and using a virtual instrument software (Labview), it carries out a program development of the system. The developed system not only integrates the functions of traditional instruments, but also describes the bearing states and the types of bearing failure accurately according to the running status of the monitored bearings. It provides technical support for the mine hoist repair and maintenance and scientific protection for its safe running.

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Brito Junior, Geraldo Carvalho, Roberto Dalledone Machado, Anselmo Chaves Neto, and Mateus Feiertag Martini. "Experimental Aspects in the Vibration-Based Condition Monitoring of Large Hydrogenerators." International Journal of Rotating Machinery 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1805051.

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Based on experimental observations on a set of twenty 700 MW hydrogenerators, compiled from several technical reports issued over the last three decades and collected from the reprocessing of the vibration signals recorded during the last commissioning tests, this paper shows that the accurate determination of the journal bearings operating conditions may be a difficult task. It shows that the outsize bearing brackets of large hydrogenerators are subject to substantial dimensional changes caused by external agents, like the generator electromagnetic field and the bearing cooling water temperature. It also shows that the shaft eccentricity of a journal bearing of a healthy large hydrogenerator, operating in steady-state condition, may experience unpredictable, sudden, and significant changes without apparent reasons. Some of these phenomena are reproduced in ordinary commissioning tests or may be noticed even during normal operation, while others are rarely observed or are only detected through special tests. These phenomena modify journal bearings stiffness and damping, changing the hydrogenerator dynamics, creating discrepancies between theoretical predictions and experimental measurements, and making damage detection and diagnostics difficult. Therefore, these phenomena must be analyzed and considered in the application of vibration-based condition monitoring to these rotating machines.

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Bujoreanu, Carmen, and Florin Breabăn. "Bearing Scuffing Detection and Condition Monitoring Using Virtual Instrumentation." Applied Mechanics and Materials 657 (October 2014): 604–8. http://dx.doi.org/10.4028/www.scientific.net/amm.657.604.

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Bearing condition monitoring confronts the most machine users. Diagnostic methods used to include bearing problems represent one of the most important challenges. The scuffing phenomenon initiation of the bearing elements produces an important increase in the vibration level and can be emphasized by the analysis of the bearing friction forces which are the most sensitive indicator of the bearing failure. Commonly used technique for damage detection is the vibration signature analysis that must be carefully utilized in conjunction with the friction torque monitoring through the strain gauges measurements. In order to detect the scuffing onset, the paper presents an experimental setup for the scuffing tests performed on a 7206 ball bearing. A virtual instrument monitoring the friction force respectively the braking torque was created. An accelerometer captures the signal from the bearing outer ring then it is processed using PCI-4451 National Instruments data acquisition board and LabVIEW soft.

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Jie Liu, W. Wang, and F. Golnaraghi. "An Enhanced Diagnostic Scheme for Bearing Condition Monitoring." IEEE Transactions on Instrumentation and Measurement 59, no. 2 (2010): 309–21. http://dx.doi.org/10.1109/tim.2009.2023814.

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Morsy, Mohamed El, and Gabriela Achtenová. "Bearing condition monitoring approaches - envelope and cepstrum analyses." International Journal of Vehicle Noise and Vibration 13, no. 3/4 (2017): 350. http://dx.doi.org/10.1504/ijvnv.2017.089531.

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Morsy, Mohamed El, and Gabriela Achtenová. "Bearing condition monitoring approaches - envelope and cepstrum analyses." International Journal of Vehicle Noise and Vibration 13, no. 3/4 (2017): 350. http://dx.doi.org/10.1504/ijvnv.2017.10010574.

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Zhi, Hou, and Zeng Jie. "Condition Monitoring Technology for Bearing Ring Groove Grinding." Journal of Physics: Conference Series 1213 (June 2019): 052036. http://dx.doi.org/10.1088/1742-6596/1213/5/052036.

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Narendiranath Babu, T., T. Manvel Raj, and T. Lakshmanan. "A Review on Application of Dynamic Parameters of Journal Bearing for Vibration and Condition Monitoring." Journal of Mechanics 31, no. 4 (2015): 391–416. http://dx.doi.org/10.1017/jmech.2015.6.

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AbstractThe journal bearings are used to support high-speed rotors in turbo machinery which often operate above the rotor first bending critical speed. This bearing provide both lateral support and dynamic coefficients: Stiffness, damping, and mass terms, related to machine vibrations. The various methods of identifying journal bearing dynamic characteristics, from measured data, obtained from different measurement systems, are reviewed. The various approaches to the bearing identification problem are discussed. The various data processing methods in the time and frequency domains are presented. Also, vibration and condition monitoring techniques are presented. In this review, the relative strengths and weaknesses of bearing are presented and developments and trends in improving bearing measurements are documented. Future trends of journal bearing are discussed.

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Sahoo, Sudarsan, J. K. Das, and Bapi Debnath. "Rolling Element Bearing Condition Monitoring using Filtered Acoustic Emission." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3560. http://dx.doi.org/10.11591/ijece.v8i5.pp3560-3567.

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The defect present in the bearing of a rolling element may affect the performance of the rotating machinery and may reduce its efficiency. For this reason the condition monitoring of a rolling element bearing is very essential. So many measuring parameters are there to diagnose the fault in a rolling element bearing. Acoustic signature monitoring is one of them. Every rolling element bearing has its own acoustic signature when it is in healthy condition and when the bearing get defected then there is a change in its original acoustic signature. This change in acoustic signature can be monitored and analyzed to detect the fault present in the bearing. But the noise present in the acquired acoustic signal may affect the analysis. So the noisy acoustic signal must be filtered before the analysis. In this work the experiment is performed in two stages. In first stage the filtration of the acquired acoustic signal is done by employing the active noise cancellation (ANC) filtering techniques. In second stage the filtered signal is used for the further analysis. For the analysis initially the static analysis is done and then the frequency and the time-frequency analysis is done to diagnose the defect in the bearing. From all the three analysis the information about the defect present in the bearing is well detected.

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Hong, Lee Chun, Abd Kadir Mahamad, and Sharifah Saon. "RetComm 1.0: Real Time Condition Monitoring of Rotating Machinery Failure." MATEC Web of Conferences 150 (2018): 01002. http://dx.doi.org/10.1051/matecconf/201815001002.

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The breakdown of motor proves to be very expensive as it increases downtime on the machines. Development of cost-effective and reliable condition monitoring system for the protection of motors to avoid unexpected breakdowns is necessary. Therefore, RetComm 1.0 is developed as assistant tool for bearing condition diagnosis system. The smartphone accelerometer is used to collect the vibration signal data and send it to computer by using the Android application named Matlab Mobile. The Matlab software is used to implement a program which is the RetComm 1.0 system to analyse the vibration signal and monitor the condition of the bearing. The algorithm used to observe the condition of bearing is trained by using Artificial Neural Network (ANN). In this project, the ANN is trained by using Matlab software. This proposed method is implemented for early diagnosis purposes. The diagnosis process can be done by just attached the smartphone onto the bearing for data collection. In conclusion, the bearing condition can be identified with this system. The bearing condition are shown in text to let the user know the bearing conditions. The raw data and power spectrum graph plotting are to let the user more further to understand the health condition of the bearing.

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Rabeyee, K., X. Tang, F. Gu, and A. D. Ball. "The Effect of Wear Evolution on Vibration-based Fault Detection in Tapered Roller Bearings." International Journal of Condition Monitoring 9, no. 1 (2019): 18–23. http://dx.doi.org/10.1784/204764297237736057.

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Rolling element bearings (REBs) are typical tribological components used widely in rotating machines. Their failure could cause catastrophic damage. Therefore, condition monitoring of bearings has always had great appeal for researchers. Usually, the detection and diagnostics of incipient bearing faults are achieved by characterising the weak periodic impacts induced by the collision of defective bearing components. However, race wear evolution, which is inevitable in bearing applications, can affect the contact between bearing elements and races, thereby decreasing the impact magnitudes and impeding detection performance. In this paper, the effect of wear evolution on the condition monitoring of rolling bearings is firstly analysed based on internal clearance changes resulting from the wear effect. Then, an experimental study is ingeniously designed to simulate wear evolution and evaluate its influence on wellknown envelope signatures according to measured vibrations from widely used tapered roller bearings. The fault type is diagnosed in terms of two indices: the magnitude variation of characteristic frequencies and the deviation of such frequencies. The experimental results indicate a signature decrease with regard to wear evolution, suggesting that accurate severity diagnosis needs to take into account both the wear conditions of the bearing and the signature magnitudes.

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Wu, Jun, Chaoyong Wu, Yaqiong Lv, Chao Deng, and Xinyu Shao. "Design a degradation condition monitoring system scheme for rolling bearing using EMD and PCA." Industrial Management & Data Systems 117, no. 4 (2017): 713–28. http://dx.doi.org/10.1108/imds-11-2016-0469.

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Purpose Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing. Design/methodology/approach The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index. Findings The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application. Research limitations/implications Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available. Practical implications The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics. Originality/value The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

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Elforjani, Mohamed. "Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition." Structural Health Monitoring 17, no. 3 (2017): 532–48. http://dx.doi.org/10.1177/1475921717704620.

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The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

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Nasim Khan Raja, Babar, Saeed Miramini, Colin Duffield, Shilun Chen, and Lihai Zhang. "A Simplified Methodology for Condition Assessment of Bridge Bearings Using Vibration Based Structural Health Monitoring Techniques." International Journal of Structural Stability and Dynamics 21, no. 10 (2021): 2150133. http://dx.doi.org/10.1142/s0219455421501339.

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The mechanical properties of bridge bearings gradually deteriorate over time resulting from daily traffic loading and harsh environmental conditions. However, structural health monitoring of in-service bridge bearings is rather challenging. This study presents a bridge bearing condition assessment framework which integrates the vibration data from a non-contact interferometric radar (i.e. IBIS-S) and a simplified analytical model. Using two existing concrete bridges in Australia as a case study, it demonstrates that the developed framework has the capability of detecting the structural condition of the bridge bearings in real-time. In addition, the results from a series of parametric studies show that the effectiveness of the developed framework is largely determined by the stiffness ratio between bridge bearing and girder ([Formula: see text], i.e. the structural condition of the bearings can only be effectively captured when the value of [Formula: see text] ranges from 1/100 and 100.

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Cornel, Daniel, Francisco Gutiérrez Guzmán, Georg Jacobs, and Stephan Neumann. "Condition monitoring of roller bearings using acoustic emission." Wind Energy Science 6, no. 2 (2021): 367–76. http://dx.doi.org/10.5194/wes-6-367-2021.

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Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic-emission system towards lubricating conditions. In addition, the system has shown that a damaged surface can be detected at least ∼ 4 % (8 h, regarding the time to failure) earlier than by using the vibration-based system. Furthermore, significant deviations from the average acoustic-emission signal were detected up to ∼ 50 % (130 h) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.

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Xiangyang, Li, and Chen Wanqiang. "Rolling Bearing Fault Diagnosis Based on Physical Model and One-Class Support Vector Machine." ISRN Mechanical Engineering 2014 (April 14, 2014): 1–4. http://dx.doi.org/10.1155/2014/160281.

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This paper aims at diagnosing the fault of rolling bearings and establishes the system of dynamics model with the consideration of rolling bearing with nonlinear bearing force, the radial clearance, and other nonlinear factors, using Runge-Kutla such as Hertzian elastic contactforce and internal radial clearance, which are solved by the Runge-Kutta method. Using simulated data of the normal state, a self-adaptive alarm method for bearing condition based on one-class support vector machine is proposed. Test samples were diagnosed with a recognition accuracy over 90%. The present method is further applied to the vibration monitoring of rolling bearings. The alarms under the actual abnormal condition meet the demand of bearings monitoring.

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Grishchenko, A. V., and О. R. Khamidov. "Monitoring and diagnostics of the technical condition of the asynchronous traction motor of locomotives using artificial neural networks on the railways of the Republic of Uzbekistan." Proceedings of Petersburg Transport University 17, no. 4 (2020): 514–24. http://dx.doi.org/10.20295/1815-588x-2020-4-514-524.

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Objective: Diagnostics of malfunctions of rolling bearings of an asynchronous traction electric motor (ATEM) of locomotives using artifi cial neural networks. Methods: To control and diagnose the technical condition of the ATEM bearing units of locomotives, a hardware-software complex and data analysis methods are used. Results: We investigated the malfunctions of the ATEM rolling bearing of locomotives. The analysis of failures of locomotive bearing units is carried out. Vibration and current signals and the corresponding frequency spectra of an ATEM operating under normal conditions and with various bearing faults are considered. A model for assessing the technical condition of rolling bearings of locomotives has been developed, and the importance of anticipatory diagnostics has been substantiated, which makes it possible to identify defects in advance at the earliest stage of their development. Practical importance: The results of the research can be used in the system for diagnosing the technical condition of rolling bearings of traction electric motors of locomotives in real time.

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Wan, Biao, Jianguo Yang, and Qinghe Wang. "Evaluation of Tribological Properties of Bearing Materials for Marine Diesel Engines Utilising the Contact Voltage Method." Applied Sciences 11, no. 17 (2021): 7811. http://dx.doi.org/10.3390/app11177811.

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The contact voltage (CV) method, which can detect miniature failures, has been tested under laboratory conditions to monitor the condition of bearings. In this study, the bearing materials for marine diesel engines, aluminium and copper alloy, were tested on a bearing fatigue wear test bench in the boundary lubrication state, which was found through tests of the different parameters. The frictional torque, the oil film thickness and the bearing temperature were measured, as well as the CV signals. The possibility of using the CV technique to monitor the condition of the bearings was also assessed by evaluating the tribological properties. After 10 h of the test, the aluminium alloy bearing was worn to the alloy layer. Then, the wear-reducing layer on the surface of the bearing slowly peeled off, and the wear was intensified. Due to its higher wear-resisting property, the amount of wear on the copper alloy bearing increased slowly. After 20 h of the fatigue wear test, the aluminium alloy bearing became severely worn, the CV characteristic was up to 81% of the initial value, the bearing temperature increased by 6.3%, and the torque value increased by 32%. This indicates that the CV method is more sensitive to wear failure. Due to better wear resistance, the copper alloy bearing showed only slightly wear and a small increase in its CV value. The main contribution is that the CV method is useful for monitoring the lubricated condition and for evaluating the tribological properties of bearings. This research has laid technical foundations for the engineering of the sliding bearing wear monitoring system based on the CV method.

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Georgiadis, Anthimos, Xiaoyun Gong, and Nicolas Meier. "Vibration analysis based on the spectrum kurtosis for adjustment and monitoring of ball bearing radial clearance." MATEC Web of Conferences 211 (2018): 06006. http://dx.doi.org/10.1051/matecconf/201821106006.

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Vibration signal analysis is a common tool to detect bearing condition. Effective methods of vibration signal analysis should extract useful information for bearing condition monitoring and fault diagnosis. Spectral kurtosis (SK) represents one valuable tool for these purposes. The aim of this paper is to study the relationship between bearing clearance and bearing vibration frequencies based on SK method. It also reveals the effect of the bearing clearance on the bearing vibration characteristic frequencies This enables adjustment of bearing clearance in situ, which could significantly affect the performance of the bearings. Furthermore, the application of the proposed method using SK on the measured data offers useful information for predicting bearing clearance change. Bearing vibration data recorded at various clearance settings on a floating and a fixed bearing mounted on a shaft are the basis of this study

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Ma, Lun, Jian She Kang, and Chun Yu Zhao. "Research on Condition Monitoring of Bearing Health Using Vibration Data." Applied Mechanics and Materials 226-228 (November 2012): 340–44. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.340.

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A reliable condition monitoring system is very useful in a wide range of industries to detect the occurrence of incipient defects so as to prevent machinery performance degradation, malfunction and sudden failure. Among the rotating machinery, many mechanical problems are attributed due to bearing failures. So implementing condition monitoring for bearing is critically needed. Considering that most research for condition monitoring only focus on detecting the existing fault, this paper add degradation tendency prognostics into the condition monitoring process. The kernel of bearing condition monitoring method presented in this paper is related to condition features extraction and remaining useful life prediction. The former is realized by the comprehensive vibration analysis for specific fault frequencies. The latter is achieved by adaptive neuron-fuzzy inference system based on extracted degradation signal. For illustration purpose, a bearing case from NASA data repository is used to validate the feasibility of the proposed method. The result indicates that the performance degradation of bearing can be effectively monitored and the predicted remaining useful life with 5.6% relative error can be the important reference for maintenance decision making.

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Lees, A. W., Z. Quiney, A. Ganji, and B. Murray. "The use of acoustic emission for bearing condition monitoring." Journal of Physics: Conference Series 305 (July 19, 2011): 012074. http://dx.doi.org/10.1088/1742-6596/305/1/012074.

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36

FARHEEN AZIZ, SHAYELA, and ARUN KUMAR. "CONDITION MONITORING AND FAULT DIAGNOSIS OF ROLLING ELEMENT BEARING." i-manager’s Journal on Instrumentation and Control Engineering 6, no. 4 (2018): 9. http://dx.doi.org/10.26634/jic.6.4.14955.

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37

Wang, C., and R. X. Gao. "A virtual instrumentation system for integrated bearing condition monitoring." IEEE Transactions on Instrumentation and Measurement 49, no. 2 (2000): 325–32. http://dx.doi.org/10.1109/19.843072.

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38

Saravanan, S., G. S. Yadava, and P. V. Rao. "Condition monitoring studies on spindle bearing of a lathe." International Journal of Advanced Manufacturing Technology 28, no. 9-10 (2006): 993–1005. http://dx.doi.org/10.1007/s00170-004-2449-0.

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39

Hotait, Hassane, Xavier Chiementin, and Lanto Rasolofondraibe. "AOC-OPTICS: Automatic Online Classification for Condition Monitoring of Rolling Bearing." Processes 8, no. 5 (2020): 606. http://dx.doi.org/10.3390/pr8050606.

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Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. This paper presents a methodology for automatic online implementation of fault diagnosis of rolling bearings, by AOC-OPTICS (automatic online classification monitoring based on ordering points to identify clustering structure, OPTICS). The algorithm consists of three phases namely: initialization, detection and follow-up. These phases use the combination of features extraction methods, smart ranking, features weighting and classification by the OPTICS method. Two methods have been integrated in the dimension reduction step to improve the efficiency of detection and the followed of the defect (relief method and t-distributed stochastic neighbor embedding method). Thus, the determination of the internal parameters of the OPTICS method is improved. A regression model and exponential model are used to track the fault. The analytical simulations discuss the influence of parameters automation. Experimental validation shows detection with 100% accuracy and regression models of monitoring reaching R 2 = 0.992 .

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Niknam, Seyed Ali, Victor Songmene, and Y. H. Joe Au. "PROPOSING A NEW ACOUSTIC EMISSION PARAMETER FOR BEARING CONDITION MONITORING IN ROTATING MACHINES." Transactions of the Canadian Society for Mechanical Engineering 37, no. 4 (2013): 1105–14. http://dx.doi.org/10.1139/tcsme-2013-0094.

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Bearings are important machine parts and their condition is often critical to success of an operation or process, hence there is a great need for periodic knowledge of their performance. According to reported research works in the past several years, it is believed that the extracted information from acoustic emission (AE) signals can be used for bearing condition monitoring. In this work, a novel parameter based on using the ratio of AE mean (μ) and AE standard deviation (σ), formulated as μ/σ is proposed to distinguish between lubricated and dry bearings. A heavy duty test rig was used in experimental work. Various levels of radial loads and rotational speed (ω) were applied to rotating shaft, which is connected to rolling element bearings. It was found that, except few cases, regardless of various levels of radial loads used, at higher levels of rotational speed, dry and lubricated bearings can be clearly distinguished when using proposed parameter.

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Holm-Hansen, Brian T., and Robert X. Gao. "Vibration Analysis of a Sensor-Integrated Ball Bearing." Journal of Vibration and Acoustics 122, no. 4 (2000): 384–92. http://dx.doi.org/10.1115/1.1285943.

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This paper presents an analysis of the vibrational behavior of a deep groove ball bearing with a structurally integrated force sensor. The miniaturized force sensor, accommodated within a slot on the bearing’s outer ring, provides on-line condition monitoring capability to the bearing. Analytical and finite element models were developed to predict the sensor output due to bearing dynamic load and rotational speed variations. Experimental studies were conducted on a ball bearing to validate the analytical and numerical solutions. Good agreement was found between the model-predicted sensor outputs and the experimental results. The findings validated the approach of integrated-sensing for on-line bearing condition monitoring. [S0739-3717(00)00203-8]

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Podduturi, Sai Sharath. "Condition Monitoring of Three Phase Induction Motor using Current Signature Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1946–55. http://dx.doi.org/10.22214/ijraset.2021.35491.

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In this paper we are going to see how Gabor transform is used to analyze the signal and to determine the inner and outer race of bearing faults by monitoring the condition of Induction motor using Motor Current Signature Analysis. Among the various faults bearing faults is the major problem, which cause a huge damage to induction motor, when unnoticed at developing stage. So, monitoring of bearing faults is very important and it can done by several conditions monitoring methods like thermal monitoring, vibration monitoring and more but these methods require expensive sensors or specified tools, whereas current monitoring methods doesn’t require any additional tools. Usually, this condition monitoring is used to detect the various faults like bearing faults, load faults by MCSA. If the fault is present in the motor, the frequency spectrum of the line current is different from healthy ones, the Gabor analysis detects the fault signature generated in the induction motor, by using mathematical expressions and calculate the RMS and Standard deviation values, these fault values are different from healthy ones. Through this we can identify faults.

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Guan, Zheng Rong, Jun Ping Li, and Xin Wu Wang. "Research on Condition Monitoring and Fault Diagnosis of Metallurgical Machinery and Equipment." Applied Mechanics and Materials 52-54 (March 2011): 504–10. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.504.

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Using the signal amplitude domain, frequency domain, bearing shock pulse measurement of bearings, gear boxes, motors and other rotating machinery vibration parameters for key components, through the accumulation of historical data and diagnosis, to predict in advance of equipment failure, for maintenance work to help provide a reliable and assurance purposes. The results show that the system does for the routine maintenance of production equipment has played a guiding role.

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Duque-Perez, Oscar, Carlos Del Pozo-Gallego, Daniel Morinigo-Sotelo, and Wagner Fontes Godoy. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods." Energies 12, no. 17 (2019): 3392. http://dx.doi.org/10.3390/en12173392.

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Condition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.

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Guo, Liang, Hongli Gao, Haifeng Huang, Xiang He, and ShiChao Li. "Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring." Shock and Vibration 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4632562.

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Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

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Zhang, Xing Hui, Jian She Kang, Jian Min Zhao, and Hong Zhi Teng. "Enhanced Fault Diagnosis of Bearings in Gearbox Based on Lucy-Richardson Deconvolution and Discussion." Applied Mechanics and Materials 764-765 (May 2015): 264–68. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.264.

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Bearings are one of the most important components in rotating machineries. Their failures will lead to great production loss and increase the maintenance cost. So, condition monitoring work of bearings can save and avoid the potential loss caused by bearing fault. Lucy-Richardson deconvolution (LRD) algorithm, as an image processing technique, started to be used in bearing fault diagnosis. However, only data of bearings working in electric motor are used to validate the method. In engineering cases, most bearings are working in gearbox. Therefore, the bearing fault signals are very weak compared to the gear vibration signal. It is usually difficult to detect the bearing fault in this case. LRD algorithm is used to enhance the bearing fault diagnosis and some characteristics in this case are discussed. Experiment data analysis demonstrates that LRD can enhance the periodic impulse signal effectively. Otherwise, if the desired fault signal is weak compared to non-desired signal, then, the desired fault signal will be continued weaken by LRD which is not benefit to bearing’s incipient fault detection.

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Zhang, Ying, Anchen Wang, and Hongfu Zuo. "Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors." Sensors 19, no. 4 (2019): 824. http://dx.doi.org/10.3390/s19040824.

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This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings.

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Osman, Shazali, and Wilson Wang. "An enhanced Hilbert–Huang transform technique for bearing condition monitoring." Measurement Science and Technology 24, no. 8 (2013): 085004. http://dx.doi.org/10.1088/0957-0233/24/8/085004.

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49

Katter, James G., and Jay F. Tu. "Bearing Condition Monitoring for Preventive Maintenance in a Production Environment." Tribology Transactions 39, no. 4 (1996): 936–42. http://dx.doi.org/10.1080/10402009608983615.

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Yoshioka, Takeo, and Shigeo Shimizu. "202 Condition monitoring of Rolling Bearing using Compound Diagnostic System." Proceedings of the Symposium on Evaluation and Diagnosis 2006.5 (2006): 87–90. http://dx.doi.org/10.1299/jsmesed.2006.5.87.

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