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MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis

Author

Listed:
  • Jinping Liu

    (Hunan Normal University)

  • Jie Wang

    (Hunan Normal University
    Central South University)

  • Xianfeng Liu

    (Hunan Normal University)

  • Tianyu Ma

    (Hunan Normal University)

  • Zhaohui Tang

    (Central South University)

Abstract

This paper proposes a moving window recursive sparse principal component analysis (MWRSPCA)-based online fault monitoring scheme, aim at providing an online fault monitoring solution for large-scale complex industrial processes (e.g., chemical industry processes) with time-varying and dynamically changing characteristics. It establishes a sparse principal component analysis (SPCA) model based on the sliding window block matrixes to perform process monitoring and incorporates normal process monitoring data set simultaneously to the model training set to update the monitoring model online, so that the process monitoring model has strong adaptability to time-varying processes. A recursive computing procedure of the corresponding sparse loading matrixes is derived based on a modified rank-one matrix approximation algorithm, so that the computational complexity of the process monitoring model is greatly decreased and the real-time monitoring capability can be guaranteed. The effectiveness of the proposed method is verified by the benchmark Tennessee-Eastman process. Compared with traditional fault monitoring methods, the proposed method can effectively improve the fault detection accuracies with lower false alarm rates, which is suitable for the fault monitoring of time-varying, long-term and continuous complex industrial processes.

Suggested Citation

  • Jinping Liu & Jie Wang & Xianfeng Liu & Tianyu Ma & Zhaohui Tang, 2022. "MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1255-1271, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-020-01721-8
    DOI: 10.1007/s10845-020-01721-8
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    References listed on IDEAS

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    1. Maroua Said & Khaoula ben Abdellafou & Okba Taouali, 2020. "Machine learning technique for data-driven fault detection of nonlinear processes," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 865-884, April.
    2. Wo Jae Lee & Gamini P. Mendis & Matthew J. Triebe & John W. Sutherland, 2020. "Monitoring of a machining process using kernel principal component analysis and kernel density estimation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1175-1189, June.
    3. Gerardo Emanuel Granados & Loïc Lacroix & Kamal Medjaher, 2020. "Condition monitoring and prediction of solution quality during a copper electroplating process," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 285-300, February.
    4. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
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