联合指标独立成分分析在多变量过程故障诊断中的应用

樊继聪; 王友清; 秦泗钊

doi:10.3724/SP.J.1004.2013.00494

联合指标独立成分分析在多变量过程故障诊断中的应用

doi: 10.3724/SP.J.1004.2013.00494

1.
北京化工大学信息科学与技术学院北京 100029, 中国;
2.
北京化工大学高新技术研究院北京 100029, 中国;
3.
美国南加州大学工学院洛杉矶 90089-1211, 美国

详细信息

通讯作者:
王友清

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出版历程
- 收稿日期: 2012-05-12
- 修回日期: 2012-08-14
- 刊出日期: 2013-05-20

Combined Indices for ICA and Their Applications to Multivariate Process Fault Diagnosis

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2.
High-Tech Research Institute, Beijing University of Chemical Technology, Beijing 100029, China;
3.
Viterbi School of Engineering, University of Southern California, Los Angeles, 90089-1211, USA

摘要

摘要: 作为主成分分析(Principal component analysis, PCA)和因子分析(Factor analysis, FA)的扩展, 独立成分分析(Independent component analysis, ICA)已经在多变量过程故障诊断中得到了很多的应用和发展. ICA的监测指标通常有三个(I2、Ie2和SPE), 使用起来不如一个指标方便, 且分散了故障信息.本文利用三个指标的加权和, 提出了两种联合的ICA监测指标. 本文进一步对比分析了不同指标的统计意义和物理意义, 并在仿真数据中验证了联合指标的优势, 在TE过程中验证了其检测和诊断特性.
- 多变量过程 /
- 故障诊断 /
- 独立成分分析 /
- 联合指标
Abstract: As a development of principal component analysis (PCA) and factor analysis (FA), independent component analysis (ICA) has been applied effectively to multivariate process monitoring and fault diagnosis and has got many excellent achievements. Usually, ICA has three indices for monitoring and diagnosis, i.e., I2, Ie2, and SPE, and the multi-indexes make the monitoring and diagnosis inconvenient and also decentralizes the fault influence. In this paper, two combined indices for ICA are developed, both of which are weighted sums of the three indices. The statistics and physical meanings of all indices are analyzed and compared. Based on the simulation tests on a numerical example and TE process, the proposed combined indices have some advantages compared with the traditional multi-indices.
- Multivariate process /
- fault diagnosis /
- independent component analysis /
- combined index

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参考文献(1)

[1]

Zhou Dong-Hua, Li Gang, Li Yuan. Data Driven Industrial Process Fault Diagnosis Technology —— Based on Principal Component Analysis and Partial Least Squares. Beijing: Science Press, 2011(周东华, 李钢, 李元. 数据驱动的工业过程故障诊断技术——基于主元分析与偏最小二乘的方法. 北京: 科学出版社, 2011)[2] Liu Qiang, Chai Tian-You, Qin Si-Zhao, Zhao Li-Jie. Progress of data-driven and knowledge-driven process monitoring and fault diagnosis for industry process. Control and Decision, 2010, 25(6): 801-807, 813(刘强, 柴天佑, 秦泗钊, 赵立杰. 基于数据和知识的工业过程监视及故障诊断综述. 控制与决策, 2010, 25(6): 801-807, 813)[3] Qin S J. Statistical process monitoring: basics and beyond. Journal of Chemometrics, 2003, 17(8-9): 480-502[4] Alcala C F, Qin S J. Reconstruction-based contribution for process monitoring with kernel principal component analysis. Industrial and Engineering Chemistry Research, 2010, 49(17): 7849-7857[5] Ge Z Q, Yang C J, Song Z H. Improved kernel PCA-based monitoring approach for nonlinear processes. Chemical Engineering Science, 2009, 64(9): 2245-2255[6] Lee J M, Yoo C K, Choi S W, Vanrolleghem P A, Lee I B. Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 2004, 59(1): 223-234[7] Dong D, McAvoy T J. Nonlinear principal component analysis-based on principal curves and neural networks. Computers and Chemical Engineering, 1996, 20(1): 65-78[8] Cheng C Y, Hsu C C, Chen M C. Adaptive kernel principal component analysis (KPCA) for monitoring small disturbances of nonlinear processes. Industrial and Engineering Chemistry Research, 2010, 49(5): 2254-2262[9] Misra M, Yue H H, Qin S J, Ling C. Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers and Chemical Engineering, 2002, 26(9): 1281-1293[10] Shao R, Jia F, Martin E B, Morris A J. Wavelets and non-linear principal components analysis for process monitoring. Control Engineering Practice, 1999, 7(7): 865-879[11] Yao Y, Gao F R. Batch process monitoring in score space of two-dimensional dynamic principal component analysis (PCA). Industrial and Engineering Chemistry Research, 2007, 46(24): 8033-8043[12] Chang Yu-Qing, Wang Shu, Tan Shuai, Wang Fu-Li, Yang Jie. Research on multistage-based MPCA modeling and monitoring method for batch processes. Acta Automatica Sinica, 2010, 36(9): 1312-1320(常玉清, 王姝, 谭帅, 王福利, 杨杰. 基于多时段MPCA模型的间歇过程监测方法研究. 自动化学报, 2010, 36(9): 1312-1320)[13] Tan Shuai, Wang Fu-Li, Chang Yu-Qing, Wang Shu, Zhou He. Fault detection of multi-mode process using segmented PCA based on differential transform. Acta Automatica Sinica, 2010, 36(11): 1626-1635(谭帅, 王福利, 常玉清, 王姝, 周贺. 基于差分分段PCA的多模态过程故障监测. 自动化学报, 2010, 36(11): 1626-1635)[14] Westerhuis J A, Kourti T, Macgregor J F. Analysis of multiblock and hierarchical PCA and PLS models. Journal of Chemometrics, 1998, 12(5): 301-321[15] Zhang Y W, Zhou H, Qin S J. Decentralized fault diagnosis of large-scale processes using multiblock kernel principal component analysis. Acta Automatica Sinica, 2010, 36(4): 593-597[16] Hyvrinen A, Oja E. Independent component analysis: algorithms and applications. Neural Networks, 2000, 13(4-5): 411-430[17] Yang Zhu-Qing, Li Yong, Hu De-Wen. Independent component analysis: a survey. Acta Automatica Sinica, 2002, 28(5): 762-772(杨竹青, 李勇, 胡德文. 独立成分分析方法综述. 自动化学报, 2002, 28(5): 762-772)[18] Ge Z Q, Song Z H. Batch process monitoring based on multilevel ICA-PCA. Journal of Zhejiang University Science A, 2008, 9(8): 1061-1069[19] Zhang Y W. Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis (KICA) and support vector machine (SVM). Industrial and Engineering Chemistry Research, 2008, 47(18): 6961-6971[20] Lee J M, Yoo C K, Lee I B. Statistical process monitoring with independent component analysis. Journal of Process Control, 2004, 14(5): 467-485[21] Yue H H, Qin S J. Reconstruction-based fault identification using a combined index. Industrial and Engineering Chemistry Research, 2001, 40(20): 4403-4414[22] Martin E B, Morris A J. Non-parametric confidence bounds for process performance monitoring charts. Journal of Process Control, 1996, 6(6): 349-358[23] Ku W F, Storer R H, Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 179-196[24] Downs J J, Vogel E F. A plant-wide industrial process control problem. Computers and Chemical Engineering, 1993, 17(3): 245-255

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