Mine-used Motor Fault Diagnosis Based on Support Vector Machine

A method based on Motor Current Signal Analysis (MCSA) and Support Vector Machine (SVM) was presented and applied to the early faults diagnosis in induction motors used in the mine. After the stator current being sampled, the fault feature was extracted from the sampling data through FFT and used as the input of the SVM. A multi-class fault classifier was constructed to identify different faults, which was based on one to one algorithm and mixed matrix combination. Experiment results show that Support Vector Machine (SVM) has good performance for classifying non-linear and high dimension and small sample set. This method improves the accuracy in rotor fault diagnosis.