A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection

Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time-frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time-frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time-frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel's subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time-frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.