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A BMFO-KNN based intelligent fault detection approach for reciprocating compressor

Author

Listed:
  • Amitkumar Patil

    (Malaviya National Institute of Technology)

  • Gunjan Soni

    (Malaviya National Institute of Technology)

  • Anuj Prakash

    (Tata Consultancy Service)

Abstract

Reciprocating compressors are one of the critical components in petrochemical and process industries. In order to minimize the downtime and reliability enhancement of the system, it is crucial to develop an intelligent fault detection approach that can identify system anomalies before it progresses into severe faults or leads to system break down. Existing studies suggest that signal processing and machine learning technique based combined fault detection models have produced effective results. However, quality of results produced by such approach heavily depends on the input features. Hence, it is imperative to build an effective fault detection model that intelligently overcomes the shortcomings of high dimensionality and efficiently predicts the impending faults. In order to do so, a binary Moth Flame Optimization (a swarm intelligence algorithm) based wrapper-type feature selection approach combined with K-nearest neighbours-based fault classification approach is proposed for fault detection in a reciprocating compressor. The proposed model produced superior results compared to contemporary feature selection techniques (such as, particle swarm optimization, grey wolf optimization, principal component analysis) combined with K-nearest neighbours. The proposed approach can intelligently eliminate redundant features and detect faults in reciprocating compressor at minimal false alarm rate ( 99%).

Suggested Citation

  • Amitkumar Patil & Gunjan Soni & Anuj Prakash, 2022. "A BMFO-KNN based intelligent fault detection approach for reciprocating compressor," 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. 13(2), pages 797-809, June.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:2:d:10.1007_s13198-021-01395-2
    DOI: 10.1007/s13198-021-01395-2
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    References listed on IDEAS

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