基于变量概率信息的因子分析监控方法

doi:10.11949/j.issn.0438-1157.20170057

化工学报 ›› 2017, Vol. 68 ›› Issue (7): 2844-2850.DOI: 10.11949/j.issn.0438-1157.20170057

基于变量概率信息的因子分析监控方法

胡婷婷, 王帆, 侍洪波

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

收稿日期:2017-01-13 修回日期:2017-03-23 出版日期:2017-07-05 发布日期:2017-07-05
通讯作者: 侍洪波
基金资助:
国家自然科学基金项目（61374140，61673173）；中央高校基本科研业务费专项资金（222201714031）；中央高校基本科研业务费重点科研基地创新基金项目（222201717006）。

Factor analysis process monitoring method based on probabilistic information of variables

HU Tingting, WANG Fan, SHI Hongbo

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2017-01-13 Revised:2017-03-23 Online:2017-07-05 Published:2017-07-05
Contact: 10.11949/j.issn.0438-1157.20170057
Supported by:
supported by the National Natural Science Foundation of China (61374140, 61673173) and Fundamental Research Funds for the Central Universities (222201714031, 222201717006).

摘要/Abstract

摘要：

因子分析（factor analysis，FA）将噪声因素加入到建模过程中，可通过最大期望（expectation maximum，EM）算法建立模型。传统的FA（ST）指标仅利用了变量的期望信息而忽略了更能代表不确定性的方差信息，这可能会导致故障的漏报。通过对过程变量的概率分析，从本质上揭示了FA（ST）的这一缺陷。建模过程中的另一个重要因素是确定因子个数，使得在降维的同时能最大程度地保留对过程有用的信息。针对传统监控指标信息不足的问题，提出的负对数似然概率（negative log likelihood probability，NLLP）指标整合了更全面的概率信息；针对因子个数给定的问题，提出了一种整体-局部因子数确定法，使得因子和变量对于过程的信息解释率都达到收敛。最后通过数值例子和Tennessee Eastman（TE）过程验证了所提方法的有效性和优越性。

关键词: 因子分析, 参数估计, 过程控制, 信息解释率, 统计分析

Abstract:

Factor analysis (FA), which noise factors are taken into consideration, can establish probabilistic generative model by the expectation maximum (EM) algorithm. However, traditional FA (ST) index may lead to missed fault alarms by utilizing only expectation information of variables and ignoring variance information of variables that is more representative of uncertainty. The drawback of FA (ST) index was revealed by probabilistic analysis of process variables. Another important part in the modeling process was to determine number of factors, which could preserve most useful process information in the meantime of dimension reducing. A negative log likelihood probability (NLLP) index, which integrated more comprehensive probabilistic information, was proposed to overcome dilemma of insufficient information of traditional monitoring index. For the determination of number of factors, a novel global-local method was introduced so that information explanation ratios of global factors and variables over process information reached convergence simultaneously. Numerical simulation and Tennessee Eastman (TE) process study illustrated effectiveness and superiority of the proposed method.

Key words: factor analysis, parameter estimation, process control, information explanation ratio, statistical analysis

中图分类号:

TP277

胡婷婷, 王帆, 侍洪波. 基于变量概率信息的因子分析监控方法[J]. 化工学报, 2017, 68(7): 2844-2850.

HU Tingting, WANG Fan, SHI Hongbo. Factor analysis process monitoring method based on probabilistic information of variables[J]. CIESC Journal, 2017, 68(7): 2844-2850.

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基于变量概率信息的因子分析监控方法

Factor analysis process monitoring method based on probabilistic information of variables

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