Load Value Analyse of Stepter Motor Fault Diagnose Based on PSO-LSSVM

In this paper, the least square support vector machine(LSSVM) fault diagnosis method based on load value analysis is adopted, the load feedback value is filtered by wavelet packet decomposition, and the eigenvector is extracted. Particle swarm optimization optimizes the kernel parameters c and g of LSSVM algorithm, and the optimal LSSVM model based on load value analysis is obtained. The running fault of stepping motor is predicted by this model. The experimental results show that the optimal LSSVM model based on load value analysis has higher classification accuracy and smaller computation time. It is an effective way to classify step motor faults.