JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 110-117.doi: 10.6040/j.issn.1672-3961.0.2017.176

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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

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

CLC Number: 

  • 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.
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