基于记忆加权核主成分分析的化工过程故障检测

doi:10.16265/j.cnki.issn1003-3033.2026.04.1571

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (4): 168-175.doi: 10.16265/j.cnki.issn1003-3033.2026.04.1571

基于记忆加权核主成分分析的化工过程故障检测

袁帅¹^,²(), 杨春曦¹^,²^,^**(), 张秀峰¹^,², 王娴¹^,², 李根根¹^,²

¹ 昆明理工大学机电工程学院, 云南昆明 650500
² 云南省先进装备智能控制及应用国际联合实验室, 云南昆明 650500

收稿日期:2025-11-11 修回日期:2026-02-05 出版日期:2026-04-28
通信作者:
**杨春曦(1976—),男,贵州铜仁人,博士,教授,主要从事复杂机电系统智能控制以及大数据技术研究。E-mail:ycx@kmust.edu.cn。
作者简介:
袁帅 (1998—),男,河南南阳人,硕士研究生,主要研究方向为化工过程故障检测和诊断、数据分析。E-mail:2403091313@qq.com。
基金资助:
国家自然科学基金资助(62463014); 云南省重大科技项目(202202AG050002); 云南省重大科技项目(202302AD080005)

Fault detection for chemical process based on memory-weighted kernel principal component analysis

Yuan Shuai¹^,²(), Yang Chunxi¹^,²^,^**(), Zhang Xiufeng¹^,², Wang Xian¹^,², Li Gengen¹^,²

¹ Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming Yunnan 650500, China
² Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment, Kunming Yunnan 650500, China

Received:2025-11-11 Revised:2026-02-05 Published:2026-04-28

摘要/Abstract

摘要：

为提高对化工过程中常见故障的检测能力,保证化工过程平稳可靠运行,基于历史故障数据信息和核主成分分析法(KPCA),提出一种利用历史故障数据加权的新型故障检测方法,即记忆加权核主成分分析法(M-WKPCA)。首先,根据KPCA计算正常数据和历史故障数据的核主成分(KPC),对比构建的指标选择出能够突出故障信息的KPC,并利用求取的加权矩阵突出故障信息,构建新的统计量,进而建立基于M-WKPCA方法的在线故障检测模型;然后,利用M-WKPCA方法,将检测不同常见故障的M-WKPCA方法并行处理,提出一种能够高精度检测常见故障的并行WKPCA故障检测策略;最后,采用田纳西伊斯曼(TE)化工过程数据仿真验证文中所提方法。结果表明:所提方法在测试数据中平均准确检测率为82.25%,远高于对比方法,在故障检测上更有优势;同时,由于KPC的选择引入了故障信息,检测的故障数据与正常数据在统计量上区分明显。

关键词: 记忆加权核主成分分析(M-WKPCA), 核主成分分析(KPCA), 化工过程, 核密度估计(KDE), 故障检测

Abstract:

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.

Key words: memory-weighted kernel principal component analysis(M-WKPCA), kernel principal component analysis(KPCA), chemical process, kernel density estimation(KDE), fault detection

中图分类号:

X937

袁帅, 杨春曦, 张秀峰, 王娴, 李根根. 基于记忆加权核主成分分析的化工过程故障检测[J]. 中国安全科学学报, 2026, 36(4): 168-175.

Yuan Shuai, Yang Chunxi, Zhang Xiufeng, Wang Xian, Li Gengen. Fault detection for chemical process based on memory-weighted kernel principal component analysis[J]. China Safety Science Journal, 2026, 36(4): 168-175.

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故障序号	PCA	KPCA	DWPCA	M-WKPCA	P-WKPCA
1	0.992 7	0.993 8	0.994 8	0.996 9	0.999 0
2	0.985 4	0.984 4	0.977 0	0.986 5	0.987 5
3	0.212 5	0.245 8	0.299 6	0.249 0	0.249 0
4	0.760 4	0.852 1	0.991 6	0.997 9	0.997 9
5	0.396 9	0.431 3	0.990 6	0.996 9	0.999 0
6	0.994 8	0.994 8	0.996 9	0.999 0	1
7	0.996 9	0.994 8	0.764 0	1	1
8	0.980 2	0.977 1	0.802 7	0.989 6	0.991 7
9	0.206 3	0.214 8	0.190 0	0.191 7	0.230 2
10	0.530 2	0.581 3	0.793 3	0.868 8	0.868 8
11	0.672 9	0.721 9	0.833 0	0.812 5	0.829 2
12	0.986 5	0.980 2	0.986 4	0.981 3	0.995 8
13	0.954 2	0.955 2	0.838 2	0.962 5	0.962 5
14	0.996 9	0.994 8	0.999 0	0.999 0	0.999 0
15	0.226 0	0.266 7	0.295 4	0.255 2	0.309 4
16	0.386 5	0.455 2	0.788 1	0.909 4	0.909 4
17	0.865 6	0.880 2	0.913 3	0.966 7	0.969 8
18	0.911 5	0.910 4	0.913 3	0.919 8	0.924 0
19	0.307 3	0.388 5	0.181 6	0.750 0	0.766 7
20	0.587 5	0.641 7	0.762 0	0.815 6	0.816 7
21	0.526 0	0.531 3	0.422 8	0.625 0	0.685 4
平均值	0.689 4	0.714 1	0.749 2	0.822 5	0.832 9

基于记忆加权核主成分分析的化工过程故障检测

Fault detection for chemical process based on memory-weighted kernel principal component analysis

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