Abstract：Aiming at the non-stationary and nonlinear characteristics of diesel engine cylinder head vibration signals, this paper proposes a diesel engine hybrid fault diagnosis method based on the combination of dispersion entropy-improved variational modal decomposition (DVMD) and stacked sparse autoencoder (SSAE). The dispersion entropy is used to determine the decomposition number K value of VMD, and the effective modal components are selected according to the turning point of the dispersion entropy. Extract 14 common time-domain features and energy features after wavelet packet decomposition from the selected modal components, construct a hybrid multi-feature vector, and input it into a deep neural network (DNN) based on stacked sparse autoencoders and Softmax layers. The recognition of 7 mixed failure modes of diesel engine is realized. Comparing with other common methods, the results show that the method proposed in this paper can effectively extract fault features and has a higher diagnostic accuracy.
白雲杰1,2，贾希胜1,2，梁庆海1,2. 基于DVMD和SSAE的柴油机混合故障诊断[J]. 振动与冲击, 2022, 41(11): 271-277.
BAI Yunjie1,2, JIA Xisheng1,2, LIANG Qinghai1,2. Hybrid fault diagnosis of Diesel engine based on DVMD and SSAE. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(11): 271-277.
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