基于卷积脉冲神经网络的故障诊断方法研究

doi:10.16088/j.issn.1001-6600.2021070808

Abstract

Abstract: Deep learning provides ideas for the intelligent development of bearing fault diagnosis. From the perspective of brain-like computing, this paper designs spiking neural network sensitive to bearing data to complete the task of fault data classification. First, signal decomposition is used to improve the feature extraction effect of the original signal, and then the fault signal is spiking-encoded, and the time step is filled with multi-channel chaotic input as the input of the neural network. Finally, the Spiking convolutional neural network (SCNN) is used. In order to verify the classification effect of the model, a classification experiment is done on the CWRU bearing data set, and the classification accuracy rate reaches 99.78%. The results show that the bearing data encoding scheme can give full play to the spatiotemporal dynamic characteristics of the SNN, and the SNN model has the characteristics of high precision and high efficiency in bearing fault diagnosis. This research is helpful to promote the research and application of SNN in the field of fault diagnosis.

Key words: SNN, multimodal decomposition, rolling bearing, fault diagnosis, IIR filter

CLC Number:

TP183

MA Xinna, ZHAO Men, QI Lin. Fault Diagnosis Based on Spiking Convolution Neural Network[J].Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 112-120.

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Fault Diagnosis Based on Spiking Convolution Neural Network

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