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Bearing fault diagnosis method based on SNN-LSTM under limited samples

Published:2023-03-23 author:LV Yun-kai, WU Bing, LI Cong-ming. Browse: 432 Check PDF documents

Bearing fault diagnosis method based on SNN-LSTM under limited samples

LV Yun-kai1, WU Bing1,2, LI Cong-ming1,2

(1.School of Mechanical and Transportation Engineering, Taiyuan University of Technology,

Taiyuan 030024, China; 2.Key Laboratory of New Sensors and Intelligent Control of Ministry

of Education, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract: The realization of fault diagnosis methods based on deep learning required the use of a large number of labeled training samples, and in the case of small sample data, the use of these methods would cause the problem of model underfitting, and the classification accuracy obtained was also low.In order to solve the above problems,a bearing fault diagnosis method combining Siamese neural network (SNN) and long and short time memory network (LSTM) was proposed under limited samples. Firstly, a pair of original vibration signal sample pairs with positive or negative labels were used as the input of the diagnosis method, and the number of training samples could be expanded by comparing the similarity between a pair of input samples.Then,the method of sharing the network parameters of extracting the features of sample pairs was used and the construction process of SNN was completed. The convolution layer, pooling layer and the LSTM layer were used to extract the features of the original vibration signal. The similarity of a pair of input samples was judged by Manhattan distance between them and the bearings under different conditions were classified. Finally, in order to verify the effectiveness of the fault diagnosis method based on SNN-LSTM in bearing fault diagnosis,a test bench for bearing fault diagnosis was completed,and the bearing vibration signals under different speeds and different states were collected. The research results indicate that the accuracy of the proposed method reaches 80.57% when the number of samples is only 140, which is higher than the classical deep learning method under limited samples.

Key words: deep learning; Siamese neural network(SNN); long and short time memory network(LSTM); training samples; model underfitting; classification accuracy; Manhattan distance

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