Title: Spectral kurtosis based on evolutionary digital filter in the application of rolling element bearing fault diagnosis

Authors: Dabin Jie; Guanhui Zheng; Yong Zhang; Xiaoxi Ding; Liming Wang

Addresses: College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China; State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China ' China North Vehicle Research Institute, Beijing, 100072, China ' China North Vehicle Research Institute, Beijing, 100072, China ' College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China; State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China ' College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China; State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

Abstract: Rolling element bearings are essential components in rotating machinery. It is important to detect the bearing fault as earlier as possible. It is known that spectral kurtosis (SK) is sensitive to impulse signal and has been widely used to detect bearing fault. Whereas, the incipient fault of bearing is weak and difficult to extract especially in a complex rotating system. Focusing on this issue, this study proposed a hybrid approach using evolutionary digital filter (EDF) and SK to detect rolling element bearing fault feature. Firstly, the signal to noise ratio of the raw signal was enhanced by EDF in a self-learning process. Then, the optimal band was detected using fast SK. Envelop analysis is later employed to extract the fault characteristic frequencies. The proposed approach was verified by numerical simulation and experimental analysis. Results show that the proposed SK-based EDF yields a good accuracy in bearing fault diagnosis.

Keywords: evolutionary digital filter; EDF; spectral kurtosis; fault diagnosis; bearing.

DOI: 10.1504/IJHM.2021.114173

International Journal of Hydromechatronics, 2021 Vol.4 No.1, pp.27 - 42

Received: 07 Jul 2020
Accepted: 27 Aug 2020
Published online: 12 Apr 2021 *

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