Author
Wentao Hu
Bio: Wentao Hu is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Fault (power engineering) & Wavelet packet decomposition. The author has an hindex of 1, co-authored 1 publications receiving 127 citations.
Papers
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TL;DR: In this paper, the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters is discussed, and the effects of reducing dimension analysis and kernel principal component analysis are compared.
154 citations
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TL;DR: In this method, a de-noising algorithm of second generation wavelet transform using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR).
Abstract: In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.
111 citations
TL;DR: New compound fault features, extracted from continuous and discrete wavelet transform of vibration signal are proposed and fault classification accuracy of these features is found to be better than the conventional time and frequency domain parameters.
104 citations
TL;DR: In this article, a fault diagnosis method of planetary gear based on the entropy feature fusion of ensemble empirical mode decomposition (EEMD) is proposed, and the original feature set is composed of various entropy features of each IMF.
94 citations
TL;DR: Simulation studies on the Tennessee–Eastman (TE) benchmark process evaluate the performance of the developed method, which indicate that the DSSAE method performs better than both SAE and SSAE.
93 citations
TL;DR: A leak aperture recognition and location method based on root mean square (RMS) entropy of local mean deposition and Wigner–Ville time-frequency analysis that can effectively identify different leak apertures and the leak location accuracy is better than that of the direct cross-correlation method.
85 citations