您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 110-117.doi: 10.6040/j.issn.1672-3961.0.2017.176

• • 上一篇    下一篇

基于两步子空间划分的化工过程监测方法

杨雅伟,宋冰,侍洪波*   

  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
  • 收稿日期:2017-02-10 出版日期:2017-10-20 发布日期:2017-02-10
  • 通讯作者: 侍洪波(1965— ),男,新疆喀什人,教授,博士,主要研究方向为故障检测,诊断及工况监控.E-mail: hbshi@ecust.edu.cn E-mail:yyw@ecust.edu.cn
  • 作者简介:杨雅伟(1979— ),女,河北承德人,博士研究生,主要研究方向为故障检测,诊断及工况监控.E-mail:yyw@ecust.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61374140);国家自然科学基金资助项目(61373173);中央高校基本科研业务费专项资金资助项目(222201714031)

Chemical process monitoring based on two step subspace division

YANG Yawei, SONG Bing, SHI Hongbo*   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

PDF (PC)

583

摘要： 为了解决现代化工过程采集的数据维度高、分布复杂的问题,提出一种基于两步子空间(two step subspace division, TSSD)划分的化工过程监测方法。为了降低过程分析复杂度,将具有相似特性的变量划分为同一空间。考虑数据的复杂分布问题,将第一步得到的每个子空间划分为高斯空间与非高斯空间。利用主元分析(principal component analysis, PCA)和独立元分析(independent component analysis, ICA)方法建立检测模型并构造统计量。整合每个子空间的统计量并基于局部离群因子(local outlier factor, LOF)方法构建综合统计量。结果表明:TSSD方法对于16个故障均能取得最优的漏报率,尤其是故障10和故障16,漏报率分别为15.375%和6.75%,有效验证所提出的基于两步子空间划分的过程监测方法的优越性。

关键词: 过程监测, 两步子空间划分, 主元分析, 独立元分析, 局部离群因子

Abstract: In order to solve the problem of high dimension and complex distribution of data collected from modern chemical processes, a method for monitoring chemical process was presented based on two step subspace division(TSSD). In order to reduce the complexity of process analysis, variables with similar characteristic were divided into the same space. Considering the complex distribution of data, the subspace obtained from the first step was divided into Gaussian subspace and non-Gaussian subspace. Principal component analysis(PCA)and independent component analysis(ICA)were used to establish the detection models and construct the statistics. All statistics of subspaces were integrated and used to construct the final statistics based on local outlier factor(LOF). The process results showed that the optimal missed detection rates of TSSD can be obtained for 16 faults, especially 15.375% for fault 10 and 6.75% for fault 16. The superiority monitoring performance of the proposed two steps subspace division method was proved.

Key words: process monitoring, two step subspace division, principal component analysis, local outlier factor, independent component analysis

中图分类号: 

  • TP273
[1] PORTNOY I, MELENDEZ K, PINZON H, et al. An improved weighted recursive PCA algorithm for adaptive fault detection[J]. Control Engineering Practice, 2016, 50: 69-83.
[2] BEGHI A, BRIGNOLI L, CECCHINATO L, et al. Data-driven fault detection and diagnosis for HVAC water chillers[J]. Control Engineering Practice. 2016, 53: 79-91
[3] ZHAO Chunhui. Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring[J]. AIChE Journal, 2014, 60(2): 559-573.
[4] FEITAL T, KRUGER U, DUTRA J, et al. Modeling and performance monitoring of multivariate multimodal processes[J]. AIChE Journal, 2013, 59(5): 1557-1569.
[5] LEE J M, CHOI S W, LEE I B. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59(1): 223-234.
[6] ALCALA C F, QIN S J. Reconstruction-based contribution for process monitoring with kernel principal component analysis[J]. Industrial and Engineering Chemistry Research, 2010, 49(17): 7849-7857.
[7] SRINIVASAN R, WANG C, HO W K, et al. Dynamic principal component analysis based methodology for clustering process states in agile chemical plants[J]. Industrial and Engineering Chemistry Research, 2004, 43(18): 2123-2139.
[8] RASHID M M, YU Jie. Hidden Markov model based adaptive independent component analysis approach for complex chemical process monitoring and fault detection[J]. Industrial and Engineering Chemistry Research, 2012, 51(15): 5506-5514.
[9] GE Zhiqiang, SONG Zhihuan. Distributed PCA model for plant-wide process monitoring [J]. Industrial and Engineering Chemistry Research, 2013, 52(5): 1947-1957.
[10] 吕小条,宋冰,谭帅,等. 基于全变量信息的子空间监控方法[J]. 化工学报,2015,66(4):1395-1401. LYU Xiaotiao, SONG Bing, TAN Shuai, et al. Subspace monitoring based on full variable information[J]. Journal of Chemical Industry and Engineering, 2015, 66(4):1395-1401.
[11] MACGREGOR J F, JAECKLE C, KIPARISSIDES C, et al. Process monitoring and diagnosis by multiblock PLS methods[J]. AIChE Journal, 1994, 40(5): 826-838.
[12] TONG Chudong, SONG Yu, YAN Xuefeng. Distributed statistical process monitoring based on four-subspace construction and Bayesian inference[J]. Industrial and Engineering Chemistry Research, 2013, 52(29): 9897-9907.
[13] SONG Bing, SHI Hongbo, MA Yuxin, et al. Multisubspace principal component analysis with local outlier factor for multimode process monitoring[J]. Industrial and Engineering Chemistry Research, 2014, 53(42): 16453-16464.
[14] ZHANG Yingwei, MA Chi. Decentralized fault diagnosis using multiblock kernel independent component analysis[J]. Chemical Engineering Research and Design, 2012, 90(5): 667-676.
[15] LYU Zhaomin, YAN Xuefeng, JIANG Qingchao. Batch process monitoring based on just-in-time learning and multiple-subspace principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 137(20):128-139.
[16] GE Zhiqiang, SONG Zhihuan. Process monitoring based on independent component analysis-principal component analysis(ICA-PCA)and similarity factors[J]. Industrial and Engineering Chemist Research, 2007, 46(7): 2054-2063.
[17] ZHAO Chunhui, GAO Furong, WANG Fuli. Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis(KICA-PCA)[J]. Industrial and Engineering Chemist Research, 2009, 48(20): 9163-9174.
[18] HUANG Jian, YAN Xuefeng. Gaussian and non-gausian double subspace statistical process monitoring based on principal component analysis and independent component analysis[J]. Industrial and Engineering Chemist Research, 2015, 54(3): 1015-1027.
[19] 杨雅伟,宋冰,侍洪波. 多SVDD模型的多模态过程监控方法[J]. 化工学报,2015, 66(11): 4526-4533. YANG Yawei, SONG Bing, SHI Hongbo. Multimode processes monitoring methodvia multiple SVDD Model[J]. Journal of Chemical Industry and Engineering, 2015, 66(11): 4526-4533.
[20] MA Hehe, HU Yi, SHI Hongbo. Fault detection and identification based on the neighborhood standardized local outlier factor method [J]. Industrial and Engineering Chemistry Research, 2013, 52(6): 2389-2402.
[21] RICKER N L. Decentralized control of the tennessee eastman challenge process[J]. Journal of Process Control, 1996, 6(4): 205-221.
[22] DOWNS J J, VOGEL E F. A plant-wide industrial process control problem[J]. Computers and Chemical Engineering, 1993, 17(3): 245-255.
[23] RICKER N L. Optimal steady-state operation of the Tennessee eastman challenge process[J]. Computers and Chemical Engineering, 1995, 19(9):949-959.
[1] 张米露,王天真,汤天浩,辛斌. 一种模式关联主元分析的海流机故障检测方法[J]. 山东大学学报(工学版), 2017, 47(5): 123-129.
[2] 王梦园,张雄,马亮,彭开香. 基于因果拓扑图的工业过程故障诊断[J]. 山东大学学报(工学版), 2017, 47(5): 187-194.
[3] 李明虎,李钢,钟麦英. 动态核主元分析在无人机故障诊断中的应用[J]. 山东大学学报(工学版), 2017, 47(5): 215-222.
[4] 孙靖杰1,赵建军2*,姚跃亭3,姚刚1. 基于可变遗忘因子的改进RPCA方法及其在自适应故障监测中的应用[J]. 山东大学学报(工学版), 2012, 42(4): 60-66.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发