Motor fault diagnosis method based on spiking convolutional neural network with multi-scale decomposition local features.

Abstract

Motor fault diagnosis has been widely focused on various manufacturing systems. Traditional neural networks have limitations in extracting temporal features from data. This paper proposes a motor fault diagnosis method based on spiking convolutional neural network with multi-scale decomposition local features. This method extracts the local features of the raw motor fault signals at different scales (frequency and time) using Discrete Wavelet Transform (DWT), capturing detailed information from various frequency bands, with high-frequency instantaneous changes and low-frequency steady trends. Then, Gaussian population encoding features are used to generate time spikes, enhancing the accuracy and optimization ability of feature representation, to avoid local optima and improve the model's generalization performance. To further improve the performance of the network, Spiking Convolutional Neural Network (SCNN) is combined with Batch Normalization Through Time (BNTT). BNTT performs batch normalization at the temporal level, effectively enhancing the training stability of the neural network, reducing issues like vanishing or exploding gradients, and accelerating the convergence process. In addition, the surrogate gradient method is used to overcome the backpropagation problem in spiking neural networks, allowing the temporal neural network to be trained smoothly. Finally, the experiments and comparisons are conducted by using the Induction Motor Data Sets (IMDS) and Case Western Reserve University (CWRU) datasets. The proposed method can achieve test accuracy of 99.49 % and 96.31 % on IMDS and CWRU respectively. The results show that this method offers high test accuracy and low computational cost.