CNN based Mechanical Fault Diagnosis of High Voltage Circuit Breaker using Sound and Current Signal

摘要

High voltage circuit breaker is a critical equipment of power system. It is very important to ensure the circuit breaker to operate in a normal state. According to statistics, most defect and fault of high voltage circuit breaker is caused by mechanical faults. In this research, the sound and current signals were collected in the simulation experiment of typical mechanical faults, namely iron core jam, two kinds of tripping mechanism faults, and spring fatigue. Then the signals were down sampled, flipped and stacked to fit deep learning model. A convolution neural network (CNN) model consisting eight layers was developed to extract features and categorize faults from the pre-processed signals. The results indicate that the mechanical fault diagnosis accuracy rate is up to 94%, higher than conventional methods using sound or current signal.