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Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring and fault diagnostics of these bearings. The development of machine learning (ML) brings a new way of diagnosing the fault of rolling element bearings. In the current work, ML models, namely, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), are used to classify the faults associated with different ball bearing elements. Using open-source Case Western Reserve University (CWRU) bearing data, machine learning classifiers are trained with extracted time-domain and frequency-domain features. The results show that frequency-domain features are more convincing for the training of ML models, and the KNN classifier has a high level of accuracy compared to SVM. [ABSTRACT FROM AUTHOR]

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings. [ABSTRACT FROM AUTHOR]

Rolling contact wear is a complex phenomenon that might involve different wear mechanisms (adhesive, abrasive, fatigue and corrosive) and different stress concentration mechanisms (asperity, dent, debris, inclusions, etc.). The interactions among these mechanisms might accelerate or decelerate the overall wear progress. Therefore, it is complicated to model and monitor the fluctuations of wear progress. The current descriptive models are either describing individual physical phenomena within rolling contact wear or describing a specific stage of wear progress. Thus, the interactions among different wear mechanisms and the transition events among different stages of wear progress are not sufficiently addressed. Therefore, the purpose of this paper is to propose a descriptive model of the wear evolution process in rolling bearings over the whole lifetime. The descriptive model utilises a wide range of empirical findings in the literature to describe the wear interactions and evolution in the five-stage scenario: running-in, steady-state, defect initiation, defect propagation, and damage growth. The new descriptive model provides the most probable scenario of wear evolution in rolling bearings, which is useful for modelling and monitoring the wear progress. It illustrates the wear evolution stages, the involved wear mechanisms in each stage, the interaction among wear mechanisms in each stage, the surface topology changes and the influencing factors within each stage. For design, condition monitoring and prognosis purposes, these aspects are significantly important to understand, model, test and monitor the wear evolution process. [ABSTRACT FROM AUTHOR]

Exploring an effective assessment index is significant for performance degradation assessment, which has been proposed to realize equipments’ near-zero downtime and maximum productivity. In this paper, the Kolmogorov-Smirnov test based on an autoregressive model is proposed to assess the performance degradation of rolling bearings. Accelerated life test (in Hangzhou Bearing Test and Research Center) of rolling bearings was performed to collect vibration data over a whole lifetime (normal-fault-failure). The result shows that the Kolmogorov-Smirnov test method can obviously detect incipient weak defects and can reflect performance degradation process well. In particular, it can detect abnormal stages earlier before the bearing steps into failure, which is significant in condition maintenance and prognosis. [ABSTRACT FROM PUBLISHER]