摘要 : Background Tunable Q-factor wavelet transform (TQWT) is a newly developed, updated version of the wavelet transform that can break down any vibration signal into low Q-factor, high Q-factor, and residual components depending on th... 展开 Background Tunable Q-factor wavelet transform (TQWT) is a newly developed, updated version of the wavelet transform that can break down any vibration signal into low Q-factor, high Q-factor, and residual components depending on the Q-factor value. TQWT can be used for feature extraction, signal denoising, and automatic onboard defect detection in rolling element bearing fault diagnosis. Purpose This paper aims to summarize the role of TQWT as a fault diagnosis tool in recent research works on REB. Followed by a brief theoretical foundation of TQWT, the role of TQWT in fault diagnosis of REB is categorized into seven aspects: Original TQWT fault diagnosis, Improved TQWT fault diagnosis, TQWT fault diagnosis combined with other signal processing approaches. Methods TQWT fault diagnosis combined with classification algorithms, TQWT fault diagnosis combined with computational optimization techniques, TQWT fault diagnosis combined with machine learning algorithms and TQWT fault diagnosis combined with deep learning architectures. Result A brief explanation of the importance of dynamic modeling of REB is also included. Conclusion A summary of the applications of TQWT with the supporting techniques is recorded in a table at the end of this paper, it will assist the readers to understand the modern trends of TQWT in the fault diagnosis procedure of a machine component like REB. 收起