Recent advancements of signal processing and artificial intelligence in the fault detection of rolling element bearings: a review

被引:15
作者
Anwarsha, A. [1 ]
Babu, T. Narendiranath [1 ]
机构
[1] Vellore Inst Technol, Sch Mech Engn, Vellore 632014, Tamil Nadu, India
关键词
rolling element bearings; fault diagnosis; signal processing; vibration analysis; acoustic emission; artificial intelligence; machine learning; deep learning; EMPIRICAL MODE DECOMPOSITION; SUPPORT VECTOR MACHINE; DISCRETE WAVELET TRANSFORM; TIME FOURIER-TRANSFORM; ACOUSTIC-EMISSION; FEATURE-EXTRACTION; DIAGNOSIS METHOD; NEURAL-NETWORK; ROTATING MACHINERY; VIBRATION;
D O I
10.21595/jve.2022.22366
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
A rolling element bearing is a common component in household and industrial machines. Even a minor fault in this section has a negative impact on the machinery's overall operation. As a result, the industry suffers significant financial losses, and this damage can potentially result in catastrophic failures. Therefore, even a little fault in the rolling element bearings must be recognized and remedied as soon as possible. Many ways for detecting REB defects have been created in recent years, and new methods are being introduced on a daily basis. This article will provide a summary of such methods, with a focus on vibration analysis techniques. The newest advancements in this field will be recognizable to readers of this article. Anyone interested in defect diagnostics of rolling element bearings can utilize this material.
引用
收藏
页码:1027 / 1055
页数:29
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