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Analysis of Fault Detection in Rolling Element Bearings

Wilson Wang

Open publisher page 18 citations

Abstract

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.

About this research paper

What this paper is about

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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OpenAlex reports 18 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

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.

Key concepts: Bearing (navigation), Rolling-element bearing, Fault (geology), Fault detection and isolation, SIGNAL (programming language), Noise (video), Engineering, Signal processing

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