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Efficient fault detection using support vector machine based hybrid expert system

Author

Listed:
  • Buddha Kishore

    (GITAM University)

  • M. R. S. Satyanarayana

    (GITAM University)

  • K. Sujatha

    (Miracle Engineering College)

Abstract

This paper demonstrates the methodology of fault classification of rotating machinery using support vector machine (SVM) in combination with genetic algorithm and particle swarm optimization. In order to detect the machine health condition, classifier uses the features as the inputs from the preprocessed raw signal of a machine. Support vector machine classifier prepared in combination of hybrid adaptive particle swarm optimization and adaptive genetic algorithm (HAPAG) proposed for proficient flaw detection. An industrial case study of a centrifugal pump is considered and the data is given for both training and testing of the classifier. A similar study with comparable existing fault classifiers on the identification triumph is investigated. SVM based HAPAG system results in clustering the various faults with more than 90 % accuracy when compared with adaptive tuning of SVM based techniques like SVM—adaptive particle swarm optimization and SVM—adaptive genetic algorithm. The outcome indicates the adequacy of choosing the classifiers in finding the machine health condition.

Suggested Citation

  • Buddha Kishore & M. R. S. Satyanarayana & K. Sujatha, 2016. "Efficient fault detection using support vector machine based hybrid expert system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 34-40, December.
    • Handle: RePEc:spr:ijsaem:v:7:y:2016:i:1:d:10.1007_s13198-014-0281-y
    DOI: 10.1007/s13198-014-0281-y
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    Cited by:

    1. Augusto Bianchini & Marco Pellegrini & Jessica Rossi, 2019. "Maintenance scheduling optimization for industrial centrifugal pumps," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 848-860, August.
    2. Augusto Bianchini & Jessica Rossi & Lauro Antipodi, 2018. "A procedure for condition-based maintenance and diagnostics of submersible well pumps through vibration monitoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 999-1013, October.

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