Mechanical Fault Diagnosis of Circuit Breaker Based on Autoencoder Neural Network and Support Vector Machine

Mechanical faults is one of the main faults that occur in the circuit breaker. The vibration signal generated during the opening and closing process of the circuit breaker can effectively reflect its operating state. In this paper, the vibration signal of the circuit breaker under normal and fault conditions is collected by the self made online monitor of the circuit breaker, and the vibration signal is analyzed and processed by using autoencoder neural network and support vector machine. The experimental results show that the autoencoder neural network can effectively extract the characteristics of the vibration signal of the circuit breaker; the support vector machine is used to diagnose the signal, and the high accuracy is obtained on the experimental samples.