Conventional wavelet threshold methods de-noise the original signal without considering the energy distribution of signal and noise varying with decomposition layer, which reduces the efficiency and adaptability of de-noising. A novel wavelet segmented threshold method is proposed in this paper to overcome this shortcoming. This method implements a more detailed threshold processing by piecewise nonlinear threshold function, in which crucial parameter is determined adaptively by the energy distribution on the wavelet coefficients in higher decomposition layer. The simulation results indicate that the proposed method has enhancement on the signal-to-noise ratio (SNR) and reduction on the root-mean-square error (RMSE) than conventional methods. The Hilbert–Huang transformation (HHT), an effective method for analyzing nonlinear and non-stationary signal and envelope spectrum analysis are applied to the de-noised signals of the faulty bearing, and the fault characteristic frequencies are extracted and the fault types are judged effectively.
Fault Diagnosis of Rolling Element Bearing for the Traction System of High-Speed Train Based on Wavelet Segmented Threshold De-Noising and HHT
Lect. Notes Electrical Eng.
International Conference on Electrical and Information Technologies for Rail Transportation ; 2019 ; Qingdao, China October 25, 2019 - October 27, 2019
Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019 ; Kapitel : 35 ; 363-374
2020-04-04
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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