Fault diagnosis method based on a multi-scale deep convolutional neural network

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (18) : 204-211.

BIAN Jingyi,LIU Xiuli,XU Xiaoli,WU Guoxin

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Abstract

Aiming at the fault diagnosis of mechanical and electrical equipments that requires a lot of expert experience and usually has difficulty in fault identification, a method based on a multi-scale one-dimensional deep convolutional neural network (M1DCNN) was proposed to improve the original network algorithm by introducing the multi-scale processing.Firstly, several feature extraction layers with different scale convolution kernel channels were constructed in the network input layer, and the fault features in one-dimensional time series signals were extracted by multi-scale feature extraction to enrich the diagnostic information.Then, the extracted features were input into the multi-scale convolution kernel and multiple pooling layers for feature processing.Finally, the features processed by multi-channels were combined to enable the network to complete self-learning to achieve fault diagnosis.The method has been applied to the bearing fault data and planetary gearbox fault data at Case Western Reserve University.The results show that the method has the characteristics of high diagnostic accuracy and strong robustness.Compared with the original one-dimensional convolutional neural network, the accuracy rate is improved by 1.25%, and the accuracy rate is increased by more than 3% on average compared with the BP neural network and recurrent neural network.A visual analysis on the effect of network feature extraction was carried out and the results show that the model feature extraction effect and the diagnostic accuracy of the proposed method are better than the conventional one-dimensional convolutional neural network.

Key words

deep convolutional neural network(DCNN) / multi-scale feature extraction / feature visualization / fault diagnos

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BIAN Jingyi,LIU Xiuli,XU Xiaoli,WU Guoxin. Fault diagnosis method based on a multi-scale deep convolutional neural network[J]. Journal of Vibration and Shock, 2021, 40(18): 204-211

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