鲁棒PPLS模型及其在过程监控中的应用

doi:10.11949/j.issn.0438-1157.20151439

化工学报 ›› 2016, Vol. 67 ›› Issue (7): 2907-2915.DOI: 10.11949/j.issn.0438-1157.20151439

鲁棒PPLS模型及其在过程监控中的应用

陈家益, 赵忠盖, 刘飞

江南大学自动化研究所, 轻工过程先进控制教育部重点实验室, 江苏无锡 214122

收稿日期:2015-09-11 修回日期:2016-04-29 出版日期:2016-07-05 发布日期:2016-07-05
通讯作者: 赵忠盖
基金资助:
国家自然科学基金项目（61573169）；江苏省六大人才高峰项目（2014-ZBZZ-010）。

Robust PPLS model and its applications in process monitoring

CHEN Jiayi, ZHAO Zhonggai, LIU Fei

Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Institute of Automation, Jiangnan University, Wuxi 214122, Jiangsu, China

Received:2015-09-11 Revised:2016-04-29 Online:2016-07-05 Published:2016-07-05
Supported by:
supported by the National Natural Science Foundation of China (61573169) and the Six Talent Peaks Project of Jiangsu Province (2014-ZBZZ-010).

摘要/Abstract

摘要：

概率偏最小二乘（PPLS）模型建立的条件是主元和误差都服从高斯分布，但是高斯分布的期望和方差容易受到离群点的影响，导致模型的鲁棒性较差。针对PPLS模型的不足，提出一种鲁棒概率偏最小二乘（RPPLS）方法，用拖尾更宽的T分布代替高斯分布，通过调整自由度参数，使模型对含离群点数据的拟合效果更好。更进一步，将RPPLS引入过程监控中，提出GT²和GSPE两个监控指标，分别监控过程的受控状态以及模型关系的变化。PPLS和RPPLS在TE过程监控的应用结果表明RPPLS不仅能更准确检测故障的产生，而且能更有效降低故障的漏报率。

关键词: 鲁棒概率偏最小二乘算法, T分布, 参数估值, 监控指标, 模型

Abstract:

A probability model can be developed by probabilistic partial least squares (PPLS) under the conditions that both principal components and errors satisfy Gaussian distribution. However, the expectation and variance of the Gaussian distribution is susceptible to outliers. As a result, the model is not robust for the real industrial process. This paper improves the robustness of the PPLS model based on the assumption that the raw data satisfy T distribution rather than Gaussian distribution. By adjusting the freedom degree of T distribution, the proposed robust probabilistic partial least squares (RPPLS) model can overcome the shortcomings of PPLS model. Furthermore, on the basis of RPPLS model, two monitoring indicators GT² and GSPE are proposed to monitor the process state and the model changes, respectively. Comparing the monitoring performance in the TE process based on PPLS and RPPLS shows that RPPLS is more effective than PPLS in terms of the fault accuracy and the missing alarm rate.

Key words: robust probabilistic partial least squares algorithm, T distribution, parameter estimation, monitoring indices, model

中图分类号:

TP277

陈家益, 赵忠盖, 刘飞. 鲁棒PPLS模型及其在过程监控中的应用[J]. 化工学报, 2016, 67(7): 2907-2915.

CHEN Jiayi, ZHAO Zhonggai, LIU Fei. Robust PPLS model and its applications in process monitoring[J]. CIESC Journal, 2016, 67(7): 2907-2915.

参考文献 24

[1]	KANO M, NAKAGAWA Y. Data-based process monitoring process control and quality improvement: recent developments and applications in steel industry[J]. Computers & Chemical Engineering, 2008, 32(1/2): 12-24. DOI: 10.1016/j.compchemeng.2007.07.005.
[2]	李秀喜, 袁延江. 基于流程模拟的化工故障检测技术[J].化工学报, 2014, 65(11): 4472-4476.DOI: 10.3969/j.issn.0438-1157.2014.11036. LI X X, YUAN Y J. Chemical process fault detection technology based on process simulation[J].CIESC Journal, 2014, 65(11): 4472-4476.DOI: 10.3969/j.issn.0438-1157.2014.11.036.
[3]	SIMOGLOU A, MARTIN E B, MORRIS A J. Multivariate statistical process control of an industrial process fluidised-bed reactor[J]. Control Engineering Practice, 2000, 8(8): 893-909. DOI: 10.1016/S0967-0661(00)00015-0.
[4]	王海清, 宋执环, 王慧. PCA过程监测方法的故障检测行为分析[J].化工学报, 2002, 53(3): 297-301.DOI: 10.3321/j.issn: 0438-1157.2002.03.016. WANG H Q, SONG Z H, WANG H. Fault detection behavior analysis of PCA based process monimring approach[J]. Journal of Chemical Industry and Engineering(China), 2002, 53(3): 297-301.DOI: 10.3321/j.issn: 0438-1157. 2002.03.016.
[5]	WOLD S, SJÖSTRÖMA M, ERIKSSONB L. PLS-regression: a basic tool of chemometrics[J]. Chemometrics and Intelligent Laboratory Systems, 2001, 58(2): 109-130. DOI: 10.1016/S0169- 7439(01)00155-1.
[6]	MEHMOOD T, LILAND K H, SNIPEN L, et al. A review of variable selection methods in partial least squares regression[J]. Chemometrics and Intelligent Laboratory Systems, 2012, 118: 62-69. DOI: 10.1016/j.chemolab.2012.07.010.
[7]	LI S, GAO J, NYAGILO J O, et al. Probabilistic partial least square regression: a robust model for quantitative analysis of Raman spectroscopy data[C]// IEEE International Conference on Bioinformatics and Biomedicine. Atlanta: IEEE, 2011: 526-530.
[8]	LANGE K L, LITTLE R J A, TAYLOR J M G. Robust statistical modeling using the t distribution[J]. Journal of the American Statistical Association, 1989, 84(408): 881-896. DOI: 10.1080/01621459.1989.10478852.
[9]	SHOHAM S. Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions[J]. Pattern Recognition Society, 2002, 35(5): 1127-1142. DOI: 10.1016/S0031- 3203(01)00080-2.
[10]	ARCHAMBEAU C, DELANNAY N, VERLEYSEN M. Robust probabilistic projections[C]//Proceedings of 23rd International Conference on Machine Learning. New York: ACM, 2006: 33-40.
[11]	ARCHAMBEAU C, DELANNAY N, VERLEYSEN M. Mixtures of robust probabilistic principal component analyzers[J]. Neurocomputing, 2008, 71(7/8/9): 1274-1282. DOI: 10.1016/j. neucom.2007.11.029.
[12]	CHEN T, MARTIN E, MONTAGUE G. Robust probabilistic PCA with missing data and contribution analysis for outlier detection[J]. Computational Statistics and Data Analysis, 2009, 53(10): 3706-3716. DOI: 10.1016/j.csda.2009.03.014.
[13]	ZHU J L, GE Z Q, SONG Z H. Robust modeling of mixture probabilistic principal component analysis and process monitoring application[J]. AIChE Journal, 2014, 60(6): 2143-2157. DOI: 10.1002/aic.14419.
[14]	SILVA R, SCHEINES R, GLYMOURl C, et al. Learning the structure of linear latent variable models[J]. Machine Learning Research, 2006, 7(2): 191-246.
[15]	KOURTI T. Application of latent variable methods to process control and multivariate statistical process control in industry[J]. International Journal of Adaptive Control and Signal Processing, 2005, 19(4): 213-246. DOI: 10.1002/acs.859.
[16]	STOICA P, XU L Z, LI J. A new type of parameter estimation algorithm for missing data problems[J]. Statistics & Probability Letters, 2005, 75(3): 219-229. DOI: 10.1016/j.spl.2005.05.018.
[17]	WU C F. On the convergence properties of the EM algorithm[J]. The Annals of Statistics, 1983, 11(1): 95-103.
[18]	MURPHY K P. Machine Learning: A Probabilistic Perspective[M]. London: MIT Press, 2012: 46-49.
[19]	LIU C H, RUBIN D B. ML estimation of the t distribution using EM and its extensions, ECM and ECME[J]. Statistica Sinica, 1995, 5(1): 19-39.
[20]	BISHOP C M. Pattern Recognition and Machine Learning[M]. New York : Springer Press, 2006: 91-93.
[21]	KIM D, LEE I B. Process monitoring based on probabilistic PCA[J]. Chemometrics and Intelligent Laboratory Systems, 2003, 67(2): 109-123. DOI: 10.1016/S0169-7439(03)00063-7.
[22]	马贺贺, 胡益, 侍洪波.基于马氏距离局部离群因子方法的复杂化工过程故障检测[J].化工学报, 2013, 64(5): 1675-1682.DOI: 10.3969/j.issn.0438-1157.2013.05.02. MA H H, HU Y, SHI H B. Fault detection of complex chemical processes using Mahalanobis distance-based local outlier factor[J].CIESC Journal, 2013, 64(5): 1675-1682. DOI: 10.3969/j.issn. 0438-1157.2013.05.02.
[23]	CHIANG L H, RUSSELL E L, BRAATZ R D. Fault Detection and Diagnosis in Industrial Systems[M]. London: Springer London, 2001: 99-106.
[24]	QIN S J, ZHENG Y Y. Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures[J]. AIChE Journal, 2013, 59(2): 496-504.DOI: 10.1002/aic.13959.

鲁棒PPLS模型及其在过程监控中的应用

Robust PPLS model and its applications in process monitoring

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 24

相关文章 15

编辑推荐

Metrics

本文评价

[1]	宋嘉豪, 王文. 斯特林发动机与高温热管耦合运行特性研究[J]. 化工学报, 2023, 74(S1): 287-294.
[2]	连梦雅, 谈莹莹, 王林, 陈枫, 曹艺飞. 地下水预热新风一体化热泵空调系统制热性能研究[J]. 化工学报, 2023, 74(S1): 311-319.
[3]	金正浩, 封立杰, 李舒宏. 氨水溶液交叉型再吸收式热泵的能量及分析[J]. 化工学报, 2023, 74(S1): 53-63.
[4]	李科, 文键, 忻碧平. 耦合蒸气冷却屏的真空多层绝热结构对液氢储罐自增压过程的影响机制研究[J]. 化工学报, 2023, 74(9): 3786-3796.
[5]	王浩, 王振雷. 基于自适应谱方法的裂解炉烧焦模型化简策略[J]. 化工学报, 2023, 74(9): 3855-3864.
[6]	于旭东, 李琪, 陈念粗, 杜理, 任思颖, 曾英. 三元体系KCl + CaCl₂ + H₂O 298.2、323.2及348.2 K相平衡研究及计算[J]. 化工学报, 2023, 74(8): 3256-3265.
[7]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[8]	闫琳琦, 王振雷. 基于STA-BiLSTM-LightGBM组合模型的多步预测软测量建模[J]. 化工学报, 2023, 74(8): 3407-3418.
[9]	李锦潼, 邱顺, 孙文寿. 煤浆法烟气脱硫中草酸和紫外线强化煤砷浸出过程[J]. 化工学报, 2023, 74(8): 3522-3532.
[10]	郭雨莹, 敬加强, 黄婉妮, 张平, 孙杰, 朱宇, 冯君炫, 陆洪江. 稠油管道水润滑减阻及压降预测模型修正[J]. 化工学报, 2023, 74(7): 2898-2907.
[11]	刘春雨, 周桓宇, 马跃, 岳长涛. CaO调质含油污泥干燥特性及数学模型[J]. 化工学报, 2023, 74(7): 3018-3027.
[12]	李艳辉, 丁邵明, 白周央, 张一楠, 于智红, 邢利梅, 高鹏飞, 王永贞. 非常规服役超临界锅炉的微纳尺度腐蚀动力学模型建立及应用[J]. 化工学报, 2023, 74(6): 2436-2446.
[13]	刘起超, 周云龙, 陈聪. 起伏振动垂直上升管气液两相流截面含气率分析与计算[J]. 化工学报, 2023, 74(6): 2391-2403.
[14]	毕恩哲, 李双喜, 沙廉翔, 刘登宇, 陈凯放. 高温动压涨圈密封结构参数多目标优化分析[J]. 化工学报, 2023, 74(6): 2565-2579.
[15]	王承泽, 顾凯丽, 张晋华, 石建轩, 刘艺娓, 李锦祥. 硫化协同老化零价铁增效去除水中Cr（Ⅵ）的作用机制[J]. 化工学报, 2023, 74(5): 2197-2206.