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Induction Motor Fault Diagnosis and Classification Through Sparse Representation

[+] Author Affiliations
Jianjing Zhang, Peng Wang, Ruqiang Yan, Robert X. Gao

Case Western Reserve University, Cleveland, OH

Chuang Sun

Xi'an Jiaotong University, Xi'an, China

Paper No. DSCC2017-5259, pp. V002T04A005; 7 pages
doi:10.1115/DSCC2017-5259
From:
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME

abstract

Condition monitoring and fault diagnosis of induction motor play a critical role in operation safety and production efficiency. In recent study, sparse representation has demonstrated its simplicity in training, robustness to noise and high accuracy in classification. This paper evaluates the effectiveness of sparse representation as an alternative approach to induction motor fault diagnosis with fault classification rate and robustness to noise as performance measure. Aiming at eliminating the human intervention in fault characteristic frequency detection and extensive feature extraction steps in traditional method, the spatial pattern of the vibration signal is studied as the classifier input. The residual sparsity index (RSI) is proposed to quantify the degree of multi-class data separation and evaluate the reliability of classification results. Experimental results show that the sparse representation method using vibration signal achieves high motor multi-fault classification accuracy and good robustness to noise, with no human intervention required for fault characteristic pattern detection and the need for long feature extraction eliminated. Finally, RSI confirms the high overall reliability of classification results.

Copyright © 2017 by ASME

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