Sequential Mechanical Fault Diagnosis in High Voltage Circuit Breaker using Attention Mechanism

Abstract

To address the shortcomings of traditional shallow-based model in terms of feature extraction and generalization capacity, this paper provides a high-voltage circuit breaker mechanical condition online detection scheme by using attention mechanism to locally weight the sample correlation and combining convolutional neural network (CNN) and long short-term memory (LSTM) network. The network uses convolutional layers for feature transformation of the raw vibration data, combined with the local time-domain feature representation capability of the gated unit, to extract fault-sensitive features. The temporal deep learning model using the attention mechanism enables it to extract global features of long-time sequences while making important features obtain higher weight parameters. The resulting model improves the overall learning and recognition efficiency. The experiments on the vacuum circuit breaker show that the proposed model has better performance than the classical diagnostic method based on the support vector machine.