Time–frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets. (April 2019)

• Record Type: Journal Article
• Authors: Zhang, Xin; Liu, Zhiwen; Wang, Jiaxu; Wang, Jinglin
• Is Part Of: ISA transactions. Volume 87(2019)
• Page Start: 225
• Page End: 234
• Publication Date: 2019-04
• Subjects: Bearing fault diagnosis -- Time–frequency analysis -- Continuous wavelet transform -- Multiple Q-factor Gabor wavelets -- Time–frequency resolution; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2018.11.033
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Rolling element bearings are key and also vulnerable machine elements in rotating machinery. Fault diagnosis of rolling element bearings is significant for guaranteeing machinery safety and functionality. To accurately extract bearing diagnostic information, a time–frequency analysis method based on continuous wavelet transform (CWT) and multiple Q-factor Gabor wavelets (MQGWs) (termed CMQGWT) is introduced in this paper. In the CMQGWT method, Gabor wavelets with multiple Q-factors are adopted and sets of the continuous wavelet coefficients for each Q-factor are combined to generate time–frequency map. By this way, the resolution of the CWT time–frequency map can be greatly increased and the diagnostic information can be accurately identified. Numerical simulation is carried out and verified the effectiveness of the proposed method. Case studies and comparisons with the continuous Morlet wavelet transform (CMWT) and the tunable Q-factor wavelet transform (TQWT) demonstrate the effectiveness and superiority of the CMQGWT for bearing diagnostic information extraction and fault identification. Highlights: A time–frequency analysis method termed CMQGWT is proposed for bearing fault diagnosis. Gabor wavelets with multiple Q-factors are introduced and used in the proposed method. Numerical simulation shows the method provides a time–frequency map with super resolution. Cases demonstrate the advantages of the method for bearing diagnostic information extraction.