Mechanical fault diagnosis for L-V circuit breakers based on energy spectrum entropy of wavelet packet and Naive Bayesian classifier

The paper has extracted the energy spectrum entropy of wavelet packet as the eigen vector of fault patterns, through analyzing the vibration signal in the decomposition of wavelet packet when Low-Voltage (LV) Circuit Breaker broke down. Based on the concept of Clustering Center, a Naïve Bayesian classifier has been constructed. By using the weight of probability measure, the correlations between the eigen vector has been described. Thus the simulated fault diagnosis of the LV circuit breaker has been achieved. Through simulating, the efficiency of the method has been verified, which could fasten the computing speed, optimize the real-time performance and classification precision comparing with the neural network which uses black-box modeling.