A Novel Capsule Convolutional Neural Network with Attention Mechanism for High-Voltage Circuit Breaker Fault Diagnosis

Abstract

The traditional fault diagnosis method of high-voltage circuit breakers (HVCBs) based on deep learning largely depends on the huge data size. When the data sample size is insufficient, it is difficult to meet the needs of diagnosis performance. To improve the diagnostic performance, a one-dimensional attention convolutional capsule neural network is proposed in this paper. First, to take advantage of the time fine-grained information of the vibration signals, a one-dimensional convolutional neural network with a wide convolution kernel is designed, thus avoiding the tedious manual feature extraction of the original data. Then, an attention mechanism is added in the pooling layer to change the weight allocation of vibration signal fragments and increase its feature extraction capability. Finally, a capsule layer is introduced between the convolution layer and the fully connected layer to represent feature vectorization, which ensures the integrity of fault feature extraction. The classification structure digital capsule is obtained by the feature transfer of dynamic routing. The experiment results show that the combination of the attention mechanism and capsule layer is feasible and the accuracy can reach 94.7% when the number of samples is small. Compared with the traditional method, the proposed method still has high diagnostic accuracy and anti-noise ability in the case of small sample.