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Fault identification modal and its application based on ISAM-Drsnet
Published:2024-03-06 author:ZHU Lewen, TIAN Xing, LI Xianhua. Browse: 1225 Check PDF documents
ault identification modal and its application based on ISAM-Drsnet

ZHU Lewen1,2, TIAN Xing1,2, LI Xianhua1,2
(1.School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China; 2.Institute of Artificial 
Intelligence and Big Data, Anhui University of Science and Technology, Huainan 232001, China)

Abstract: Aiming at the difficulty of extracting effective features of the network model in a complex environment during rolling bearing fault diagnosis, and the problem of not being able to fully mine the time series characteristics of periodic rolling bearing fault data, a rolling bearing fault diagnosis model of improved strip attention mechanism-deep residual shrinkage network (ISAM-Drsnet) was proposed. Firstly, recurrence plots (RP) encoding method was used to generate two-dimensional images, and the ISAM and improved soft threshold algorithm were used to strengthen Drsnet. Then, the data set was enhanced by overlapping sampling, and the data were input into ISAM-Drsnet to realize identification of different fault types. Finally, through experiments on the rolling bearing data set of Case Western Reserve University, the optimal data interception length was selected, and the influence of improved soft threshold, data set size, and noise on the model was studied. At the same time, comparative analysis was conducted with models such as support vector machine (SVM), backpropagation neural network (BPNN), convolutional neural network (CNN), and performance evaluation was conducted using visualization methods such as confusion matrix. The experimental results show that the fault diagnosis performance of this method is significantly better than that of SVM, BPNN, CNN and other models, and the fault diagnosis accuracy can reach 99.79%. The accuracy is 1.60% higher than the original Drsnet network model. And in the case of limited data set size and noise added to the signal, the model still has high stability. The research results show that the bearing fault diagnosis model not only has excellent diagnosis performance, but also has strong robustness.
Key words:  rolling bearing; fault diagnosis performance; improved strip attention mechanism(ISAM); deep residual shrinkage network(Drsnet); recurrence plots(RP); robustness
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