方位(导航)
滚动轴承
断层(地质)
故障检测与隔离
信号(编程语言)
噪音(视频)
工程类
信号处理
状态监测
降噪
计算机科学
电子工程
人工智能
声学
振动
电气工程
数字信号处理
地质学
物理
地震学
执行机构
程序设计语言
图像(数学)
出处
期刊:IEEE Instrumentation & Measurement Magazine
[Institute of Electrical and Electronics Engineers]
日期:2021-05-01
卷期号:24 (3): 42-49
被引量:7
标识
DOI:10.1109/mim.2021.9436098
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.
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