Multi-sparsity-based blind deconvolution and its application to wheelset bearing fault detection. (August 2022)

• Record Type: Journal Article
• Authors: Gu, Xiaohui; Yang, Shaopu; Liu, Yongqiang; Hao, Rujiang; Liu, Zechao
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Wheelset bearing -- Blind deconvolution -- Multi-objective optimization -- Repetitive transients; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111449
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A multi-sparsity-based blind deconvolution is proposed for extracting the repetitive transients. A knee point driven multi-objective particle swarm optimization algorithm is given. Experiments verify its superior than single-sparsity-based methods in vibration fault detection. Abstract: Fault detection of wheelset bearing is important to maintain the safety of high-speed train. The key in detecting the fault is to recover repetitive transients under heavy background noise from wheel/rail contacting. In this paper, a multi-sparsity-based blind deconvolution technique is proposed for vibration fault detection. Instead of maximizing impulsiveness nor cyclostationarity in finding an optimal filter, a comprehensive and compromised consideration of these two aspects is taken to increase the sparsity of time- and frequency-domain simultaneously. Based on this framework, smoothness index and Gini index are used in measuring the multi-sparsity. Then a knee point-driven strategy is incorporated to extend particle swarm optimization algorithm to solve the multi-objective blind deconvolution. The proposed approach is validated using a simulated signal and two cases of real wheelset bearing vibration signals. The comparative studies also demonstrate its robustness than some peer blind deconvolution techniques as well as envelope analysis methods.