Fault Diagnosis of Conventional Circuit Breaker Contact System Based on Time–Frequency Analysis and Improved AlexNet

Abstract

In order to eliminate the influence of parameter predefined caused by manual feature extraction, achieve fast feature extraction, and improve the recognition rate of fault diagnosis, a 2-D convolution neural network (CNN) method for fault diagnosis of conventional circuit breaker contact system is proposed. First, by introducing the data preprocessing method of continuous wavelet transform (CWT), the nonlinear and nonstationary original vibration signal is transformed into a time–frequency image to extract the transformed image features. Second, the convolutional layer module in the AlexNet model is combined with the network in network (NIN) module, and the global average pooling (GAP) layer is adopted to replace the fully connected (FC) layer, which realizes the improvement of the traditional AlexNet model. Then, an improved Adam optimization algorithm, namely, AMSGrad, is adopted to solve the problem that the Adam optimization algorithm may not converge or produce local optimization during model training. Finally, the preprocessed time–frequency image is taken as the input of the improved AlexNet model and through the supervised adjustment of network parameters, the fault diagnosis of the contact system for the conventional circuit breaker is realized accurately.