Analysis of Fault Detection in Rolling Element Bearings

被引：0
|
作者：
Wang, Wilson [1 ]
机构：
[1] Lakehead University, Department of Mechanical Engineering, Ontario, Canada
来源：
IEEE Instrumentation and Measurement Magazine | 2021年 / 24卷 / 03期
关键词：
Condition based maintenance - Signal processing - Condition monitoring - Defects - Fault detection - Signal to noise ratio;
D O I：
10.1109/MIM.2021.9436098
中图分类号：
学科分类号：
摘要：
Rolling-element bearings are commonly used in rotary machinery. As a matter of fact, most machinery imperfections are related to bearing defects. Reliable bearing fault detection techniques are very useful in industries for predictive maintenance operations. Bearing fault detection still remains a very challenging task especially when defects occur on rotating bearing components because the fault-re-lated features could be nonstationary in nature. In this paper, the recent development of bearing fault detection and the challenges facing reliable bearing health condition monitoring will be discussed. Specifically, the paper will discuss the bearing characteristic frequency analysis, denoising to improve the signal-to-noise ratio, and advanced signal processing techniques for nonstationary signal analysis and bearing fault detection. © 1998-2012 IEEE.
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页码：42 / 49
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