基于互信息的分散式动态PCA故障检测方法

doi:10.11949/j.issn.0438-1157.20160218

化工学报 ›› 2016, Vol. 67 ›› Issue (10): 4317-4323.DOI: 10.11949/j.issn.0438-1157.20160218

基于互信息的分散式动态PCA故障检测方法

童楚东, 蓝艇, 史旭华

宁波大学信息科学与工程学院, 浙江宁波 315211

收稿日期:2016-02-29 修回日期:2016-07-14 出版日期:2016-10-05 发布日期:2016-10-05
通讯作者: 童楚东
基金资助:
国家自然科学基金项目（61503204）；浙江省自然科学基金项目（Y16F030001）；浙江省公益技术研究项目（2015C31017）；浙江省信息与通信工程重中之重学科开放基金项目（XKXL1526）。

Fault detection by decentralized dynamic PCA algorithm on mutual information

TONG Chudong, LAN Ting, SHI Xuhua

Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, Zhejiang, China

Received:2016-02-29 Revised:2016-07-14 Online:2016-10-05 Published:2016-10-05
Supported by:
supported by the National Natural Science Foundation of China (61503204), the Natural Science Foundation of Zhejiang Province (Y16F030001), the Science & Technology Planning Project of Zhejiang (2015C31017) and the Key Discipline Fund of Information and Communication Engineering of Zhejiang (XKXL1526).

摘要/Abstract

摘要：

对现代大型复杂动态过程来讲，不同测量变量会存在不同的序列相关性，而且变量间的相互影响会体现在不同的采样时刻上。为此，结合利用分散式建模的优势，提出一种基于互信息的分散式动态过程故障检测方法。该方法在对每个测量变量都引入多个延时测量值后，利用互信息为每个变量区分出与其相关的测量值，并建立起相应的变量子块。这种变量分块方式使每个变量子块都能充分地获取与之相对应的自相关性与交叉相关性信息，较好地处理了数据的动态性问题。然后，利用主元分析（PCA）算法对每一变量子块进行统计建模从而建立起适于大规模动态过程的多模块化的故障检测模型。最后，通过实例验证该方法用于动态过程监测的可行性和有效性。

关键词: 主元分析, 过程系统, 互信息, 故障检测, 统计过程监测

Abstract:

For modern large-scale complex dynamic processes, different measured variables have their own serial correlations and interactions among these variables show on different time points. A mutual information based dynamic fault detection method was proposed by an advantageously decentralized modeling strategy. After made multiple time-delayed observations on each variable, the relevant measurements for the variable were separated from all observations by utilizing mutual information and corresponding variable sub-blocks were created. This approach of variable grouping allowed each variable sub-block to capture sufficient information about its own self-and inter-correlations such that the dynamic characteristics of process data could be well analyzed. The principal component analysis (PCA) algorithm was employed to construct statistical modeling on each variable sub-block and a decentralized dynamic fault detection model for large-scale dynamic process. The feasibility and effectiveness of the proposed method on dynamic process modeling were validated by a case study of a chemical process.

Key words: principal component analysis, process systems, mutual information, fault detection, statistical process monitoring

中图分类号:

TP277

童楚东, 蓝艇, 史旭华. 基于互信息的分散式动态PCA故障检测方法[J]. 化工学报, 2016, 67(10): 4317-4323.

TONG Chudong, LAN Ting, SHI Xuhua. Fault detection by decentralized dynamic PCA algorithm on mutual information[J]. CIESC Journal, 2016, 67(10): 4317-4323.

参考文献 22

[1]	GE Z, ONG Z, GAO F. Review of recent research on data-based process monitoring[J]. Industrial & Engineering Chemistry Research, 2013, 52(10):3543-3562.
[2]	YIN S, DING S X, XIE X, et al. A review on basic data-driven approaches for industrial process monitoring[J]. IEEE Transactions on Industrial Electronics, 2014, 61(11):6418-6428.
[3]	QIN S J. Statistical process monitoring:basics and beyond[J]. Journal of Chemometrics, 2003, 17(7/8):480-502.
[4]	童楚东, 史旭华. 基于互信息的PCA方法及其在过程监测中的应用[J]. 化工学报, 2015, 66(10):4101-4106. TONG C D, SHI X H. Mutual information based PCA algorithm with application in process monitoring[J]. CIESC Journal, 2015, 66(10):4101-4106.
[5]	KU W, STORER R H, GEORGAKIS C. Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics & Intelligent Laboratory Systems, 1995, 30(1):179-196.
[6]	FAN J, WANG Y. Fault detection and diagnosis of nonlinear non-Gaussian dynamic processes using kernel dynamic independent component analysis[J]. Information Science, 2014, 259:369-379.
[7]	KERKHOF P V D, GINS G, VANLAER J, et al. Dynamic model-based fault diagnosis for (bio)chemical batch processes[J]. Computers & Chemical Engineering, 2012, 40:12-21.
[8]	LI G, QIN S J, ZHOU D. A new method of dynamic latent-variable modeling for process monitoring[J]. IEEE Transactions on Industrial Electronics, 2014, 61(11):6438-6445.
[9]	ZHANG Y, ZHOU H, QIN S J, et al. Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares[J]. IEEE Transactions on Industrial Informatics, 2010, 6(1):3-10.
[10]	ZHANG Y, MA C. Decentralized fault diagnosis using multiblock kernel independent component analysis[J]. Chemical Engineering Research Design, 2012, 90(5):667-676.
[11]	TONG C, SONG Y, YAN X. Distributed statistical process monitoring based on four-subspace construction and Bayesian inference[J]. Industrial & Engineering Chemistry Research, 2013, 52(29):9897-9907.
[12]	GE Z, SONG Z. Distributed PCA model for plant-wide process monitoring[J]. Industrial & Engineering Chemistry Research, 2013, 52(5):1947-1957.
[13]	JIANG Q, WANG B, YAN X. Multiblock independent component analysis integrated with Hellinger distance and Bayesian inference for non-Gaussian plant-wide process monitoring[J]. Industrial & Engineering Chemistry Research, 2015, 54(9):2497-2508.
[14]	LI W. Mutual information functions versus correlation functions[J]. Journal of Statistical Physics, 1990, 60(5/6):823-837.
[15]	HAN M, REN W, LIU X. Joint mutual information-based input variable selection for multivariate time series modeling[J]. Engineering Applications of Artificial Intelligence, 2015, 37:250-257.
[16]	DOWNS J J, VOGEL E F. A plant-wide industrial process control problem[J]. Computers & Chemical Engineering, 1993, 17(3):245-255.
[17]	VERRON S, TIPLICA T, KOBI A. Fault detection and identification with a new feature selection based on mutual information[J]. Journal of Process Control, 2008, 18(5):479-490.
[18]	JIANG Q, YAN X. Plant-wide process monitoring based on mutual information-multiblock principal component analysis[J]. ISA Transactions, 2014, 53(5):1516-1527.
[19]	GE Z, SONG Z. Multimode process monitoring based on Bayesian method[J]. Journal of Chemometrics, 2009, 23(12):636-650.
[20]	赵忠盖, 刘飞. 因子分析及其在过程监控中的应用[J]. 化工学报, 2007, 58(4):970-974. ZHAO Z G, LIU F. Factor analysis and its application to process monitoring[J]. Journal of Chemical Industry and Engineering (China), 2007, 58(4):970-974.
[21]	TONG C, PALAZOGLU A, YAN X. Improved ICA for process monitoring based on ensemble learning and Bayesian inference[J]. Chemometrics & Intelligent Laboratory Systems, 2014, 135:141-149.
[22]	韩敏, 张占奎. 基于改进核主成分分析的故障检测与诊断方法[J]. 化工学报, 2015, 66(6):2139-2149. HAN M, ZHANG Z K. Fault detection and diagnosis method based on modified kernel principal component analysis[J]. CIESC Journal, 2015, 66(6):2139-2149.

基于互信息的分散式动态PCA故障检测方法

Fault detection by decentralized dynamic PCA algorithm on mutual information

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