IFA和FA联合方法在化工过程监控中的应用
A Combined Method of Independent Factor Analysis and Factor Analysis for Chemical Process Monitoring
-
摘要: 工业过程数据具有高斯和非高斯混合分布的特点。独立因子分析（IFA）采用一维高斯混合模型拟合任意的因子分布，因此可以处理高斯和非高斯混合的问题。虽然在给定因子数的前提下变分IFA算法可以有效地缩短建模时间，但是独立因子数的选择仍然需要较长的计算时间。此外，若IFA的因子数选择不当，会造成部分因子的信息遗留在观察变量的残差中，导致GSPE监控指标的监控性能变差。为了解决IFA在实际应用中存在的问题，本文结合了IFA和FA方法。首先使用FA辅助IFA选取独立因子数，以进一步减小IFA建模时间；其次使用FA对IFA的残差进行再处理，以解决由于独立因子数选择不当造成的问题。最后将该方法应用于田纳西-伊斯曼（TE）过程和乙烯裂解炉过程的监控中，实验结果验证了该联合方法的有效性。Abstract: Many industrial process variables have the characteristics of non-Gaussian and Gaussian mixture distribution.Independent factor analysis (IFA) algorithm utilizes one dimension Gaussian mixture model to approximate any factor distribution such that it can address the problem of the Gaussian and non-Gaussian mixture.Although the variational IFA with given factors can reduce the modeling-time,it still takes a lot of time to determine an optimal number.Especially,an inappropriate number may make the information of partial factors be remained in the residuals of the observed variables,which will result in the poor monitoring performance on the GSPE index.Aiming at the above problem in the application of IFA,this paper proposes a combined method of IFA and FA.Firstly,FA algorithm is used in determining the independent factor number so as to reduce the modelling time of IFA.And then,FA is further utilized to re-process the residual of IFA so that the remained partial factors' information in residual can be fully employed.Finally,the monitoring experiment in the Tennessee-Eastman (TE) process and the ethylene cracking furnace verifies the validity of the proposed method.
-
[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] HUANG Jian,YAN Xuefeng.Dynamic process fault detection and diagnosis based on dynamic principal component analysis,dynamic independent component analysis and Bayesian inference[J].Chemometrics and Intelligent Laboratory Systems,2015,148:115-127. [3] HUANG Jian,YAN Xuefeng.Double-step block division plant-wide fault detection and diagnosis based on variable distributions and relevant features[J].Journal of Chemometrics,2015,29(11):587-605. [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] 赵忠盖,刘飞.动态因子分析模型及其在过程监控中的应用[J],化工学报,2009,60(1):183-186.
[6] HYVÄRINEN A,OJA E.Independent component analysis algorithms and applications[J].Neural Networks,2000,13(4):411-430. [7] 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. [8] 刘雪琴,谢磊,张建明,等.基于ICA-SVDD的统计过程监控及其在化工过程中的应用[C]//2006全国石油化工生产安全与控制学术交流会.北京:中国化工学会,2006:21-25.
[9] GE Zhiqiang,SONG Zhihuan.Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors[J].Industrial & Engineering Chemistry Research,2007,46(7):2054-2063. [10] 尹雪岩,刘飞.基于DIFA的动态非高斯过程监控方法及应用[J].化工学报,2011,62(5):1345-1351.
[11] ATTIAS H.Independent factor analysis[J].Neural Computation,1999,4(11):803-851. [12] 尹雪岩,刘飞.独立因子分析方法在过程监控中的应用[J].计算机与应用化学,2010,27(10):1353-1356.
[13] 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. [14] 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.
[15] SHEN Y,DING S X,HAGHANI A,et al.A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process[J]. Journal of Process Control,2012,22(9):1567-1581. [16] DOWNS J J,VOGEL E F.A plant-wide industrial process control problem[J].Computers & Chemical Engineering,1993,17(3):245-255. [17] JIANG Q,YAN X.Probabilistic weighted NPE-SVDD for chemical process monitoring[J].Control Engineering Practice,2014,28:74-89. [18] BATHELT A,RICKER N L,JELALI M.Revision of the Tennessee Eastman process model[J].International Symposium on Advanced Control of Chemical Processes,2015,48(8):309-314. [19] 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.
[20] JIANG Q,YAN X.Non-Gaussian chemical process monitoring with adaptively weighted independent component analysis and its applications[J].Journal of Process Control,2013,23(9):1320-1331. [21] JIANG Q,YAN X,LV Z,et al.Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description[J].International Journal of Production Research,2014,52(11):3273-3286. [22] 杨正永,王昕,王振雷.基于LTSA和联合指标的非高斯过程监控方法及应用[J].化工学报,2015,66(4):1370-1379.
-
下载:
点击查看大图
图(1)
计量
- 文章访问数: 1329
- HTML全文浏览量: 222
- PDF下载量: 377
- 被引次数: 0