Robust post-processing time frequency technology and its application to mechanical fault diagnosis

• Published in: Scientific reports Vol. 14; no. 1; pp. 20456 - 30
• Main Authors: Long, Junbo, Deng, Changshou, Wang, Haibin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.09.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Fault diagnosis; Feature extraction; Fuzzy energy; Humanities and Social Sciences; Infinite variance process; Multi-synchrosqueezing; multidisciplinary; Science; Science (multidisciplinary); Signal processing; Statistical models; Synchrosqueezing extracting; Infinite variance process; Feature extraction; Multi-synchrosqueezing; Synchrosqueezing extracting; Fuzzy energy
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-70347-0

Post-processing synchrosqueezing transform and synchroextracting transform methods can improve TFR resolution for fault diagnosis. The normal and fault signal can be described by infinite variance process, and 1 < α ≤ 2, even the background noise belongs to the process under complex conditions. The effect of traditional SST and SET methods is greatly reduced and even lost in infinite variance process environment. Several robust post-processing methods are proposed including FSET, FSSET, FSOSET and FMSST technology employing infinite variance process statistical model and FLOS, and their mathematical derivation are completed in this paper. The proposed methods are compared with the conventional methods, and the results show that the proposed methods achieve better results than the existing methods. In addition, the new methods are applied to diagnose the bearing outer race DE signals polluted by infinite variance process, the result demonstrates that they have performance advantages. Finally, the characteristics, shortcomings and application scenarios of the improved algorithms are summarized.