Wavelet Packet Transform and Support Vector Machine Based Discrimination of Roller Bearings Fault

Abstract

Fault diagnosis of roller bearings is very complex, so it is difficult to use the mathematical model to describe their faults. The fault diagnosis methods of ball bearing based on Wavelet packet transform with entropy features and support vector machine (SVM) are proposed in this paper. Wavelet packets have greater decor relation properties than standard wavelets in that they induce a finer partitioning of the frequency domain of the process generating the data. A two cycles of ball bearing fault current data is processed through wavelet packet transform to obtain wavelet coefficients and then Energy eigenvector of frequency domain are extracted by using Shannon entropy principle. Subsequently, the extracted Energy eigenvector of frequency domain are applied as inputs to SVM for roller bearings from internal fault. Fault state of ball bearing is identified by using radial basis function genetic-support vector machine. The results of the proposed new technique were found to be reliable, fast and accurate in identifying the fault condition.