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Fault detection for chemical process based on memory-weighted kernel principal component analysis
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Shuai Yuan1, 2, Chunxi Yang**, 1, 2, Xiufeng Zhang1, 2, Xian Wang1, 2, Gengen Li1, 2
China Safety Science Journal | 2026, 36(4) : 168 - 175
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China Safety Science Journal | 2026, 36(4): 168-175
• Safety Technology and Engineering •
Fault detection for chemical process based on memory-weighted kernel principal component analysis
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Shuai Yuan1, 2, Chunxi Yang**, 1, 2, Xiufeng Zhang1, 2, Xian Wang1, 2, Gengen Li1, 2
Affiliations
  • 1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming Yunnan 650500, China
  • 2Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment, Kunming Yunnan 650500, China
Published: 2026-04-28 doi: 10.16265/j.cnki.issn1003-3033.2026.04.1571
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To enhance the detection ability of common faults in the chemical process and ensure the stable and reliable operation, a new fault detection method, termed M-WKPCA, was proposed. The method was based on historical fault data and KPCA, incorporating historical fault data through a weighting mechanism. Initially, kernel principal components(KPC) of normal data and historical fault data were calculated according to KPCA. KPC that can highlight the fault information were selected based on a comparison of the constructed indexes. A weighting matrix was then used to highlight the fault information, and a new statistic was constructed to establish an online fault detection model based on M-WKPCA method. Then, the M-WKPCA method was used to detect common faults in parallel. A parallel WKPCA fault detection strategy was proposed to achieve high precision detection of common faults. Finally, the proposed method was verified using simulated data from the Tennessee Eastman (TE) chemical process. The results show that the proposed method achieves an average accurate detection accuracy of 82.25%. This is much higher than that of the comparison methods, demonstrating its superiority in fault detection. At the same time, since fault information is incorporated during the selection of KPC, the detected fault data are significantly different from the normal data in terms of statistics.

memory-weighted kernel principal component analysis(M-WKPCA)  /  kernel principal component analysis(KPCA)  /  chemical process  /  kernel density estimation(KDE)  /  fault detection
Shuai Yuan, Chunxi Yang, Xiufeng Zhang, Xian Wang, Gengen Li. Fault detection for chemical process based on memory-weighted kernel principal component analysis[J]. China Safety Science Journal, 2026 , 36 (4) : 168 -175 . DOI: 10.16265/j.cnki.issn1003-3033.2026.04.1571
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Year 2026 volume 36 Issue 4
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.04.1571
  • Receive Date：2025-11-11
  • Online Date：2026-07-08
  • Published：2026-04-28
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History
  • Received：2025-11-11
  • Revised：2026-02-05
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Affiliations
    1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming Yunnan 650500, China
    2Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment, Kunming Yunnan 650500, China
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