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Dynamic-controlled principal component analysis for fault detection and automatic recovery

Author

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  • Zheng, Niannian
  • Luan, Xiaoli
  • Shardt, Yuri A.W.
  • Liu, Fei

Abstract

To effectively implement the prognostic and health management for industrial processes, a dynamic-controlled principal component analysis (DCPCA) for pattern extraction and deviation diagnosis is proposed under the framework of multivariate statistical modelling, which can accurately detect and automatically rectify the faults. Significantly, the geometric properties of DCPCA are analysed, revealing the spatial structure relationships of different variables and how the data space is partitioned. In addition, the model relationships in DCPCA are explored, including the dynamic characteristics of time-series variables and the algebraic ones of static variables. Based on these results, statistics are derived for monitoring both the dynamic and static relationships of the process, and under the abnormal circumstance, by diagnosing the deviations between the fault pattern and the setpoint, a fault regulator for automatic recovery is designed. The case study of prognostic and health management for an industrial distillation column illustrates the advantages of DCPCA in fully extracting the process dynamics into pattern, as well as fault detection and automatic recovery.

Suggested Citation

  • Zheng, Niannian & Luan, Xiaoli & Shardt, Yuri A.W. & Liu, Fei, 2024. "Dynamic-controlled principal component analysis for fault detection and automatic recovery," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005227
    DOI: 10.1016/j.ress.2023.109608
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    References listed on IDEAS

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    2. Xiao, Yao & Peng, Cheng & Wu, Jiang & Deng, Jian, 2026. "Research on multi-factor hydrogen leak accident diagnosis and optimization of monitoring sensors’ layout through CFD-based data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 267(PA).
    3. Tang, Yiming & Ma, Pengfei & Li, Lei & Liu, Xin & Liu, Yanjun & Wang, Qiuliang, 2025. "Incipient fault detection based on double Kullback–Leibler divergence KLDattW improved by a self-attention mechanism," Reliability Engineering and System Safety, Elsevier, vol. 264(PA).
    4. Li, Tao & Han, Yongming & Duan, Xiaoyan & Ma, Bo & Geng, Zhiqiang, 2025. "Fault detection for multimode process based on local neighborhood-density standardization and ensemble serial global-local preserving projections processes," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    5. Wang, Yang & Dong, Yining, 2025. "Balancing information and predictability: A pan latent feature model for plant-wide oscillations root cause analysis," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    6. Yin, Wenzhe & Zhu, Shaomin & Xia, Hong, 2025. "Fault detection for time-varying process in nuclear power plants based on improved moving window and dynamic kernel principal component analysis," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).

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