Wavelets for fault diagnosis of rotary machines: A review with applications

Signal Processing, Volume 96, Part A, March 2014, Pages 1-15.

Ruqiang Yan, Robert X. Gao, Xuefeng Chen.

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China and

Department of Mechanical Engineering, University of Connecticut, Storrs, CT 06269, USA and

The State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China.



Over the last 20 years, particularly in last 10 years, great progress has been made in the theory and applications of wavelets and many publications have been seen in the field of fault diagnosis. This paper attempts to provide a review on recent applications of the wavelets with focus on rotary machine fault diagnosis. After brief introduction of the theoretical background on both classical wavelet transform and second generation wavelet transform, applications of wavelets in rotary machine fault diagnosis are summarized according to the following categories: continuous wavelet transform-based fault diagnosis, discrete wavelet transform-based fault diagnosis, wavelet packet transform-based fault diagnosis, and second generation wavelet transform-based fault diagnosis. In addition, some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, new wavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosis are also discussed.

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Additional Information: 

“Through a rigorous study, Dr. Yan and his co-authors have provided a comprehensive and knowledgeable review on utilizing wavelets as a mathematical tool for fault diagnosis of rotary machines that the scientific and engineering communities are aware of.  In a key scientific article contributing to excellence in engineering research, the authors’ work indicates wavelets will continue to be one of the most appealing techniques that dominate the field of rotary machine fault diagnosis. It is envisioned that this study will stimulate more and more researchers and practitioners to contribute their effort in applying wavelets to processing signals measured from rotary machines to be monitored. ”

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