Abstract:Traditional intelligent fault detection model such as neural network has some faults such as lacking of self-learning and self-organization, weak generalization ability, easy to fall into local minimum value and single. Intelligent detection algorithm of combination application can integrate advantages of different algorithms and avoid the disadvantages of single algorithm. Therefore, this paper proposes a mine based motor fault detection model based on combination of vector machine(SVM) algorithm and improved particle swarm optimization(PSO) algorithm. Firstly, the optimal parameters for SVM is got by using improved particle swarm optimization(PSO) algorithm, which has better inspiration performance and relapses into local optimal solution less. Secondly, the optimal parameters are used by SVM algorithm to train sample data for data classification, because SVM algorithm is good at pattern recognition. At last, a fault diagnosis model has built up. The experimental results show that the method can improve the accuracy of fault detection by 3.33%-17%.