Fault Diagnosis of Rolling Bearing Based on Wavelet Packet Transform and GA-Elman Neural Network

Aiming at the problem on how to improve the diagnostic rate of rolling bearing fault diagnosis models, the original vibration signal is denoised and feature extracted by wavelet packet decomposition and reconstruction, and the fault pattern recognition is realized by Elman neural network. For the problem that Elman neural network has a slow convergence rate and it is easy to fall into the local optimal value, this paper uses the genetic algorithm (GA) with global search ability to optimize the weight and threshold of the Elman neural network through the steps of selection, crossover and mutation. It is proved by experiments that the Elman neural network optimized by genetic algorithm has high diagnostic precision, and this method can be better applied to fault diagnosis of rolling bearings.