Dynamic modelling of wear evolution in rolling bearings

El‐Thalji, Idriss; Jantunen, Erkki

doi:10.1016/j.triboint.2014.11.021

Cited by 97 publications

(47 citation statements)

References 68 publications

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“…It also increases the useful life of a machine by reducing its wear. Nevertheless, adverse operating conditions and cyclic loading can lead to material fatigue in bearings, which manifests itself in the form of surface cracks and spalls [2]. These cracks and spalls, if allowed to go undetected, can lead to costly and unexpected shutdowns, which is detrimental to economic productivity.…”

Section: Introductionmentioning

confidence: 99%

“…However, fault diagnosis in 2 Shock and Vibration bearings has mostly been done through signal-based techniques. These techniques usually involve three major steps: (1) measurement of the signal that will be used for fault diagnosis (different types of signals, such as structural vibration [10,[15][16][17][18], stator current for bearings in induction motors [7,8], acoustic emissions [19][20][21][22][23][24][25], temperature [6], and more recently rotor speed [26], have been used); (2) processing the signal to extract features that are characteristic of anomalous conditions; (3) using different classifiers such as -NN, support vector machines (SVMs), or artificial neural networks (ANNs) for classifying normal and faulty signals.…”

Section: Introductionmentioning

confidence: 99%

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Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Khan

Kim

2016

Shock and Vibration

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Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A -nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Khan

Kim

2016

Shock and Vibration

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“…In the light of every acoustic parameter's physical meaning, different parameters present a distinct sensitivity and stability to different failure modes [29]. For example, root mean square (RMS) is sensitive to the failure of wear [30], while kurtosis is sensitive to failure of impact [31]. Additionally, as studied earlier on, the MWA might enhance the acoustic signal in the range of frequency that human ears are sensitive to.…”

Section: Health Monitoring Of the Rolling Bearings In Mwas Based On Tmentioning

confidence: 97%

“…In this paper, the clustering method with a high robustness and fast calculation speed is selected to fuse the flywheel condition parameters. Among many clustering methods, K-Medoids clustering is widely used due to its strong robustness, anti-noise ability, and its ability to handle abnormal values [30]. From the perspective of engineering applications, K-Medoids clustering also has a good convergence and time complexity, and the effect obtained in global searches is very good.…”

Section: Health Monitoring Of the Rolling Bearings In Mwas Based On Tmentioning

confidence: 99%

Health Monitoring on the Spacecraft Bearings in High-Speed Rotating Systems by Using the Clustering Fusion of Normal Acoustic Parameters

Wang²,

Liu³

et al. 2019

Applied Sciences

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The rolling bearings in moment wheel assemblies (MWAs) or control moment gyros (CMGs) are not only the core components in spacecrafts but also prone to failure. Therefore, a high reliability is the critical characteristic for spacecraft bearings, and long-life testing on the ground is one of the main means for bearing reliability assessment. In practical applications, a convenient and reliable method is required for monitoring the health status of abnormal bearings in MWAs during the long-life test. In this paper, a monitoring method based on the clustering fusion of normal operation acoustic parameters is proposed for the identification of abnormal bearings. Firstly, the characteristics of MWA's acoustic signal and its feasibility as a monitoring medium are clarified based on tests and modal analysis. Then statistical parameters and sound quality parameters are introduced to characterize the changes caused by bearing faults, and the root mean square (RMS), kurtosis, and sharpness parameters are selected to construct the feature vectors. The K-medoids clustering technology is used to fuse the characteristic parameters, and the safety distance for normal bearing operation can be obtained by a suitable method. Finally, the abnormal index is presented based on the excess rate and excess distance to judge the abnormal states of several types of bearings through tests. The research results indicate that the presented monitoring method based on the clustering of the normal operation acoustic parameters can not only identify various faults of the spacecraft bearing (ball defects, outer ring defects, cage instability, etc.) effectively but also give a quantitative evaluation of the severity of the abnormality.

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“…A modelbased method is based on the physical characteristics of monitored machine for the establishment of an explicit mathematical model. Various model-based diagnostic methods have been widely used in gearboxes [6][7][8] and bearings [9]. However, the model-based method is difficult to use when the system model is complex, because such systems are often difficult to describe with a precise mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Dynamic modelling of wear evolution in rolling bearings

Cited by 97 publications

References 68 publications

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Health Monitoring on the Spacecraft Bearings in High-Speed Rotating Systems by Using the Clustering Fusion of Normal Acoustic Parameters

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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