Bearing Fault Diagnosis Based on Labview

Abstract

This function of wavelet packet decomposition and the energy of each band to strike is achieved within the Labview module. Signal energy in different frequency bands within the change reflects a change in the operating state. Extract wavelet packet energy spectrum of each band, making it as a feature vector. Finally the fault are classified by SVM. The two Parameters, the kernel function parameters g of radial machine support vector machine and penalty factor C of the radial machine support vector machine, are optimally chosen, automatically and rapidly, by using the method of particle swarm algorithm, avoiding the blindness of artificial selection parameters. The Matalab program of support vector machines based on particle swarm optimization are made into COM components. Mixed programming, Labview call COM component, generated by the M file, is implemented, which is divorced from the MATLAB environment, making it good for expanding the function of Labview. The effectiveness, wavelet packet energy spectrum - PSOSVM model of the bearing fault diagnosis, is verified.