Motor's Early Fault Diagnosis Based on Support Vector Machine

2018 International Conference on Advanced Materials, Intelligent Manufacturing and Automation

材料科学;机械制造;运输工程

Li, Shu-Ying^1 ; Xue, Lei^2

College of Electrical and Power Engineering, Shanxi University, Taiyuan, China^1

State Grid Shanxi Economic Research Institute, Taiyuan, China^2

关键词: Fault feature;    High dimensions;    Mixed matrix;    Motor currents;    Multi faults;    Small samples;    Stator currents;    SVM classifiers;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/382/3/032047/pdf
DOI  :  10.1088/1757-899X/382/3/032047

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

An induction motors early fault diagnosis method was presented in this paper, based on Motor Current Spectrum Analysis (MCSA) and Support Vector Machine (SVM). After the stator current was sampled and transferred in FFT, the fault feature was extracted as the input of the SVM. The multi-fault SVM classifier was constructed based one-against-one strategy and mixed matrix combination, to perform the fault diagnosis and classification of different types of faults. Experiment results show that the method in this essay achieved good performance of classification under nonlinear, high dimension and small sample sets, which improved the accuracy in motor fault diagnosis.