Aimed at identifying the fans' working status accurately

a method using wavelet packet transform and singular value decomposition to extract the vibration signal characteristics was proposed.This method conducted wavelet packet decomposition on the vibration signal

and applied the reconstruction signals of wavelet packet decomposition coefficients to build the characteristic matrix.Then the singular value feature vector of this matrix was obtained with utilization of the singular value decomposition.Through the statistics analysis

the maximum

minimum and mean value of the singular value feature vector was taken as characteristic parameters to recognize the fan mechanical fault.Finally

a fan fault feature vector was put forward in order to diagnose the mechanical failure of fans accurately

which was the integration of the vibration signal wavelet packet singular value features of the five measuring points in the same instantaneous running state

and then the fault diagnosis of fan was achieved by applying the improved back propagation(BP)neural network.The diagnostic results show that this method can diagnose the category

severity and location of the fan mechanical failures effectively.