Reach on Micro-Motor Acoustics Fault Diagnosis Based on Loose Wavelet Neural Network

Abstract

Nicro-motor voice signal contains abundant running status information as well as vibration signal, aiming at the problem that it is difficult to obtain vibration signal in the production line of micro-motor, this paper proposes a micro-motor acoustic fault diagnosis methods based on loose wavelet neural network. Wavelet packet decomposition and reconstruction algorithm is utilized to extract micro-motor voice signals in each frequency band energy as the characteristic parameters of fault characteristic parameter samples will input to improve the BP neural network for training, build up the fault type of classifier, the realization of fault intelligent diagnosis. Application results show that a reasonable design of neural network has strong ability of fault identification; use loose micro-motor acoustic wavelet neural network fault diagnosis is feasible.