Second-order transient-extracting S transform for fault feature extraction in rolling bearings. (February 2023)

• Record Type: Journal Article
• Authors: Liu, Yi; Xiang, Hang; Jiang, Zhansi; Xiang, Jiawei
• Is Part Of: Reliability engineering & system safety. Volume 230(2023)
• Publication Date: 2023-02
• Subjects: Safety and reliability -- Time-frequency analysis -- Second-order transient-extracting s transform -- Transient feature extraction -- rolling bearing -- fault diagnosis; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108955
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: STEST is proposed to rectify the GD bias in time extracting S-transform. Time-frequency representation of fault periods can be concentrated by STEST. Two experiments are performed to detect faults in bearings. Abstract: Intelligent fault diagnosis methods can obtain promising results in ensuring the safety and reliability of key parts of rotating machinery. However, the problems are the insufficient amount of data during equipment acceptance period and the assumption that the collected data are high quality which directly affects the reliability of promising results. To solve the above problems, based on the characteristics of fault features, a time-frequency-based method is introduced to analyze the impulse components. Nevertheless, the performance of the time-frequency method is deeply relies on the selection of the window length. To avoid the influence of uncertain parameters, an accurate time-frequency analysis method named the second-order transient-extracting S transform based on the S-transform is proposed in this paper. The proposed method not only rectifies the group delay bias but also produces a highly concentrated time-frequency representation even in noise-surrounded and irrelevant components. The effectiveness of the proposed method for monitoring the health of key parts health is verified through simulated and experimental investigations. The accuracy of the proposed method in feature detection is higher than that of other methods. Graphic abstract: Image, graphical abstract