Applying wavelet packet energy entropy and neural networks to diagnose circuit breaker faults

We presented a new fault diagnosis method based on wavelet packet energy entropy and an improved neural network.A wavelet packet was used to decompress the vibration signal into different frequency bands using the theory of wavelet packet decomposition and reconstruction.Wavelet packet energy entropy was then extracted to construct characteristic vectors of signals and is used as an input of the neural network,which was optimized by genetic algorithm.Finally,the degree of confidence concept was introduced to evaluate the test results.This method was proven to be effective by the pattern recognition results of the circuit breaker fault.Furthermore,the improved neural network can recognize new fault patterns.