Bearing fault diagnosis based on multi‐band filtering. Issue 2 (2nd November 2021)

• Record Type: Journal Article
• Authors: Lu, Yixiang; Song, Zhihong; Gao, Qingwei; Zhu, De; Sun, Dong
• Is Part Of: IET science, measurement & technology. Volume 16:Issue 2(2022)
• Page Start: 101
• Page End: 117
• Publication Date: 2021-11-02
• Subjects: Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/smt2.12090
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract: Bearing is an important part of rotary machinery, which usually operates under a variety of complicated and severe conditions, and is prone to break down. To reduce and prevent the loss caused by the fault of bearings, a method based on multi‐band filtering (MBF) is proposed and applied to bearing fault diagnosis in this paper. Bearing signal is decomposed into multiple sub‐band signals by an MBF constructed from a specific prototype filter (finite impulse response filter) through cosine modulation. Then, the required sub‐band can be selected adaptively according to bearing fault frequency. To make the fault frequency more prominent, an adaptive filtering method is exploited to reduce the noise contained in the selected sub‐band. Finally, bearing fault diagnosis is realized by envelope spectrum analysis. Experimental results show that better performance in both simulated and real data are achieved by the proposed algorithm, which indicates that the proposed method can realize bearing fault diagnosis efficiently when compared to other state of the art methods.