Application of Rotating Machinery Fault Diagnosis System Based on Improved WNN

Significance of equipment fault diagnosis is mainly reflected in lower failure rate; lower maintenance costs reduce maintenance time, increase operating time. Wavelet network is the perfect combination of the theory of wavelet analysis and the theory artificial neural network; it is compatible with the superiority of the wavelet and neural networks. In this paper, the wavelet neural network based on the BP algorithm was studied. And also provides the initial parameter settings of wavelet neural network of combination of types of wavelet and study sample. It introduces the improved wavelet neural network based on the BP algorithm and applies it to the examples of rotating machinery fault diagnosis in order to avoid the low efficiency of traditional algorithm of network structure, and improve the performance of network learning.