Tensor Denoising Assisted Time-Reassigned Synchrosqueezing Wavelet Transform for Gear Fault Diagnosis
Wenyue Hu, Jing Yuan, Huiming Jiang, Qian Zhao, Chao Li, Ze Yao
Abstract
The gear fault signal is characterized by strong and/or weak impulsive features and is often accompanied by strong background noise, which makes the gear fault feature extraction more difficult. Hence, this article proposes a new time-frequency analysis (TFA) method called the tensor denoising (TD) assisted time-reassigned synchrosqueezing wavelet transform (TD-TSWT). First, to improve the accuracy of subsequent TFA, a TD method based on high-order singular value decomposition (HOSVD) is proposed as a preprocessing technique according to the cyclostationary characteristics of gear faults and the Hankel matrix. Meanwhile, combined with the advantages of sample entropy calculation, the concept of using sample entropy to determine the reasonable singular order is proposed. Second, to propose TSWT for revealing gear fault features. TSWT by introducing continuous wavelet transform (CWT) to obtain the basis function which is more similar to gear fault impulses. Moreover, according to the properties of the gear fault signal, the group delay estimation operator in the frequency domain is designed for TSWT, which is more suitable for the gear fault feature extraction. Finally, the results of simulations and engineering cases show that the method provides a useful tool for the diagnosis of gear faults.