S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
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Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Novel Fault Diagnosis Approach for Rolling-Element Bearings Based on Bispectral Analysis

Ruige Zhang, Kun-Chieh Wang, Long Wu, and Hao Gao

(Received September 6, 2021; Accepted December 1, 2021)

Keywords: fault diagnosis, rolling-element bearing, variable operation conditions, bispectral analysis

Under variable operation conditions, the fault diagnosis of rolling-element bearings encounters the problems of ambiguous characteristic frequencies and inconsistent disturbance features. Conventional diagnosis methods have adopted extra hardware or signal preprocessing to overcome these problems, which usually resulted in difficulties in implementation and incorrect diagnosis. Here, we propose a novel approach based on the strategy of extracting features that are insensitive to changes in operation conditions. This may have the advantages of preventing interference from signal resampling and preprocessing. First, we derive the bispectral expressions of vibration signals for rolling-element bearings in use. Next, we use simulated and measured experimental data detected by numerous fluid and vibration sensors to identify the proposed model. Finally, experiments on the fault diagnosis of bearings are performed to validate our proposed approach. Results show that the proposed bispectral distribution method has the advantage of insensitivity to the operation conditions. In experiments involving three levels of fault severity, our proposed diagnosis model always correctly identified the fault type of rolling-element bearings under different operating conditions. With the advantages of directly extracting the insensitive bispectral features without the requirement of additional hardware and signal preprocessing, our proposed approach is simple and easy to implement, giving it good application prospects in engineering practice.

Corresponding author: Kun-Chieh Wang

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