Condition Monitoring and Fault Diagnosis of Induction Motor Using Support Vector Machine

被引:38
作者
Patel, Rakesh A. [1 ]
Bhalja, Bhavesh R. [2 ]
机构
[1] Ganpat Univ, UVPCE, Dept Elect Engn, Ganpat Vidyanagar, Gujarat, India
[2] Indian Inst Technol, Dept Elect Engn, Roorkee 247667, Uttar Pradesh, India
关键词
conditioning monitoring; induction motor; support vector machine; total harmonic distortion; fault diagnosis; BROKEN ROTOR BARS; STATOR WINDINGS; CLASSIFICATION; LOCATION; SCHEME;
D O I
10.1080/15325008.2015.1131762
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article deals with the condition monitoring and fault diagnosis of a three-phase induction motor using a support vector machine classifier. By acquiring the three line voltages and currents of the motor in real time (experimentally on a 3-HP motor in the laboratory environment), total harmonic distortion is calculated, which in turn is used for the training of the support vector machine. A laboratory prototype has been developed through which various data have been generated by conducting extensive experiments on a healthy motor as well on a motor having faulty bearing, shorting of stator turns, and broken rotor bars with varying load conditions. The performance of the projected support vector machine-based scheme has been assessed for two kernel functions on the basis of fault classification accuracy. It can be noted that the accuracy of the radial basis function kernel is higher than that of the polynomial kernel. The proposed support vector machine-based scheme gives satisfactorily results, as the fault discrimination accuracy is found to be more than 98%. Simultaneously, it also gives an accuracy of the order of 95% for different motor design specifications, which confirms robustness of the proposed scheme.
引用
收藏
页码:683 / 692
页数:10
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