A study on a weak fault detection method based on adaptive parametric dictionary design using the Morlet wavelet

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (8) : 187-193.

DENG Feiyue1,2,QIANG Yawen2,HAO Rujiang2,MA Huaixiang2,GAO Fei2

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Abstract

Aiming at the problem that weak fault feature is difficult to detect under strong background noise, an adaptive parametric dictionary design method was proposed. The proposed method is based on the idea of local segmentation and global analysis of the analyzed signal, and two indexes, correlation function (CF) and kurtosis, were used to evaluate the local matching degree and global matching degree between the Morlet wavelet and the fault signal. Moreover, the whale optimization algorithm (WOA) was introduced to the analysis process, and wavelet parameters could be identified automatically. Then the parametric dictionary was constructed by point-by-point time-shift, and the fault feature was detected by the sparse decomposition using the orthogonal matching pursuit (OMP). The proposed method was applied to analyze a simulated fault signal and gear fault signal, the results show that this method can extract weak fault feature effectively, and the diagnosis effect is better than the classical parametric dictionary based on a correlation filtering algorithm (CFA), wavelet denoising,and based on K-SVD learning dictionary.
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sparse representation

/ parametric dictionary / fault diagnosis / Morlet wavelet

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DENG Feiyue1,2,QIANG Yawen2,HAO Rujiang2,MA Huaixiang2,GAO Fei2. A study on a weak fault detection method based on adaptive parametric dictionary design using the Morlet wavelet[J]. Journal of Vibration and Shock, 2021, 40(8): 187-193

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