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## 留言板

 引用本文: 樊继聪, 王友清, 秦泗钊. 联合指标独立成分分析在多变量过程故障诊断中的应用. 自动化学报, 2013, 39(5): 494-501.
FAN Ji-Cong, WANG You-Qing, QIN S. Joe. Combined Indices for ICA and Their Applications to Multivariate Process Fault Diagnosis. ACTA AUTOMATICA SINICA, 2013, 39(5): 494-501. doi: 10.3724/SP.J.1004.2013.00494
 Citation: FAN Ji-Cong, WANG You-Qing, QIN S. Joe. Combined Indices for ICA and Their Applications to Multivariate Process Fault Diagnosis. ACTA AUTOMATICA SINICA, 2013, 39(5): 494-501.

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

• 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.
•  [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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##### 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

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