基于LTSA和MICA与PCA联合指标的过程监控方法及应用

doi:10.11949/j.issn.0438-1157.20151374

化工学报 ›› 2015, Vol. 66 ›› Issue (12): 4895-4903.DOI: 10.11949/j.issn.0438-1157.20151374

基于LTSA和MICA与PCA联合指标的过程监控方法及应用

江伟¹, 王昕², 王振雷¹

1 华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海200237;
2 上海交通大学电工与电子技术中心, 上海 200240

收稿日期:2015-08-31 修回日期:2015-09-30 出版日期:2015-12-05 发布日期:2015-12-05
通讯作者: 王振雷
基金资助:
国家高技术研究发展计划项目(2013AA040701);国家自然科学基金重点项目(61134007);国家自然科学基金面上项目(61174118);上海市“科技创新行动计划”研发平台建设项目(13DZ2295300);上海市自然科学基金项目(14ZR1421800);流程工业综合自动化国家重点实验室开放课题基金资助项目(PAL-N201404)。

LTSA and combined index based MICA and PCA process monitoring and application

JIANG Wei¹, WANG Xin², WANG Zhenlei¹

1 Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200237, China;
2 Center of Electrical &Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2015-08-31 Revised:2015-09-30 Online:2015-12-05 Published:2015-12-05
Supported by:
supported by the National High Technology Research and Development Program of China(2013AA040701), the Key Program of National Natural Science Foundation of China (61134007), the General Program of National Natural Science Foundation of China (61174118), the Shanghai “Technology Innovation Action Plan” Development Platform for Building Projects (13DZ2295300), the Shanghai Natural Science Foundation of China (14ZR1421800) and the State Key Laboratory of Synthetical Automation for Process Industries(PAL-N201404).

摘要/Abstract

摘要：

独立成分分析(ICA)方法主要被用来对线性非高斯过程进行监控,为了提高对非高斯过程的监控效果,则利用过程数据信息对ICA的监控指标进行了改进,提出了一种改进的独立成分分析(MICA)方法。许多实际工业过程数据都具有非线性、非高斯与高斯混合分布的特点,为此提出了一种基于LTSA和MICA与PCA联合指标的过程监控的方法。首先采用局部切空间排列(LTSA)算法对样本数据进行非线性降维,然后分别用MICA和PCA方法得到非高斯与高斯统计量,对其进行加权得到新的统计量,并被用于过程监控。最后将该方法应用在田纳西-伊斯曼(TE)过程和乙烯裂解炉的过程监控中,证明了该方法的有效性。

关键词: 算法, 主元分析, 过程控制, 非高斯, 改进的独立成分分析, 局部切空间排列算法, 联合指标

Abstract:

Independent component analysis (ICA) method is mainly used to monitor linear and non-Gaussian process, a method named modified independent analysis (MICA) is proposed to improve the non-Gaussian process monitoring performance. The method uses the information of process data to modify the monitoring process monitoring performance. The method uses the information of process data to modify the monitoring index of ICA. Many industrial process variables have characteristics of nonlinear, non-Gaussian and Gaussian mixture distribution. A method which based on LTSA algorithm and the combined index is proposed to solve these problems of the industrial process. Firstly, the local tangent space alignment (LTSA) algorithm is used to achieve the nonlinear dimensionality reduction of sample data. Then MICA and PCA methods are used to obtain non-Gaussian and Gaussian statistics, and the new statistic, which is weighted by these two statistics, is used for process monitoring. Finally, the proposed method has been applied to monitor the Tennessee-Eastman(TE) process and the ethylene cracking furnace to show its efficiency.

Key words: algorithm, principal component analysis, process control, non-Gaussian, modified independent component analysis, local tangent space alignment algorithm, combined index

中图分类号:

TP277

江伟, 王昕, 王振雷. 基于LTSA和MICA与PCA联合指标的过程监控方法及应用[J]. 化工学报, 2015, 66(12): 4895-4903.

JIANG Wei, WANG Xin, WANG Zhenlei. LTSA and combined index based MICA and PCA process monitoring and application[J]. CIESC Journal, 2015, 66(12): 4895-4903.

参考文献 20

[1]	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.
[2]	Cai L, Tian X. A new process monitoring method based on noisy time structure independent component analysis [J]. Chinese Journal of Chemical Engineering, 2014, 23: 62-172.
[3]	Jiang Q, Yan X, Tong C. Double-weighted independent component analysis for non-Gaussian chemical process monitoring [J]. Industrial & Engineering Chemistry Research, 2013, 52(40): 14396-14405.
[4]	Li W, Yue H H, Valle-Cervantes S, et al. Recursive PCA for adaptive process monitoring [J]. Journal of Process Control, 2000, 10(5): 471-486.
[5]	Hyvärinen A, Oja E. Independent component analysis: algorithms and applications [J]. Neural Networks, 2000, 13(4): 411-430.
[6]	Lee J M, Qin S J, Lee I B. Fault detection of non-linear processes using kernel independent component analysis [J]. The Canadian Journal of Chemical Engineering, 2007, 85(4): 526-536.
[7]	Zhu W, Zhou J, Xia X, et al. A novel KICA-PCA fault detection model for condition process of hydroelectric generating unit [J]. Measurement, 2014, 58: 197-206.
[8]	Fan Jicong(樊继聪), Wang Youqing(王友清), Qin S Joe(秦泗钊). Combined indices for ICA and their applications to multivariate process fault diagnosis [J]. Acta Automatica Sinica(自动化学报), 2013, 39(5): 494-501.
[9]	Yang Zhengyong(杨正永), Wang Xin(王昕), Wang Zhenlei(王振雷). LTSA and combined index based non-Gaussian process monitoring and application [J]. CIESC Journal(化工学报), 2014, 66(4): 1370-1379.
[10]	Zhang Zhenyue, Zha Hongyuan. Principal manifolds and nonlinear dimension reduction via local tangent space alignment [J]. SLAM Journal of Scientific Computing, 2004, 26(1): 313-338.
[11]	Jiang Q, Yan X, Zhao W. Fault detection and diagnosis in chemical processes using sensitive principal component analysis [J]. Industrial & Engineering Chemistry Research, 2013, 52(4): 1635-1644.
[12]	Zhang Shaojie(张少捷), Wang Zhenlei(王振雷), Qian Feng(钱锋). FS-SVDD based on LTSA and its application to chemical process monitoring [J]. CIESC Journal(化工学报), 2010, 61(8): 1894-1900.
[13]	Shao J D, Rong G. Nonlinear process monitoring based on maximum variance unfolding projections [J]. Expert Systems with Applications, 2009, 36(8): 11332-11340.
[14]	Ge Zhiqiang(葛志强). Statistical process monitoring research of complex states [D]. Hangzhou: Zhejiang University, 2009.
[15]	Lee J M, Yoo C K, Lee I B. Statistical process monitoring with independent component analysis [J]. Journal of Process Control, 2004, 14(5): 467-485.
[16]	Yue H H, Qin S J. Reconstruction-based fault identification using a combined index [J]. Industrial and Engineering Chemistry Research, 2001, 40(20): 4403-4414.
[17]	Li X, Yang Y, Zhang W. Statistical process monitoring via generalized non-negative matrix projection [J]. Chemometrics and Intelligent Laboratory Systems, 2013, 121: 15-25.
[18]	Chiang L H, Russell E L, Braatz R D. Fault Detection and Diagnosis in Industrial Systems [M]. Springer, 2001.
[19]	Jiang Q, Yan X. Probabilistic weighted NPE-SVDD for chemical process monitoring [J]. Control Engineering Practice, 2014, 28: 74-89.
[20]	Wang Xiaoyang(王晓阳), Wang Xin(王昕), Wang Zhenlei(王振雷), Qian Feng(钱锋). Multiple models external analysis and Greedy- KP1M based process monitoring with multiple operation modes [J]. CIESC Journal(化工学报), 2012, 63(9): 2869-2876.

基于LTSA和MICA与PCA联合指标的过程监控方法及应用

LTSA and combined index based MICA and PCA process monitoring and application

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