Adaptive Local Outlier Probability for Dynamic Process Monitoring

doi:10.1016/j.cjche.2014.05.015

›› 2014, Vol. 22 ›› Issue (7): 820-827.DOI: 10.1016/j.cjche.2014.05.015

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Adaptive Local Outlier Probability for Dynamic Process Monitoring

Yuxin Ma, Hongbo Shi, Mengling Wang

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

收稿日期:2013-12-24 修回日期:2014-02-07 出版日期:2014-07-28 发布日期:2014-08-23
通讯作者: Hongbo Shi
基金资助:
Supported by theNational Natural Science Foundation of China (61374140), Shanghai Postdoctoral Sustentation Fund (12R21412600), the Fundamental Research Funds for the Central Universities (WH1214039), and Shanghai Pujiang Program (12PJ1402200).

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Yuxin Ma, Hongbo Shi, Mengling Wang

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:2013-12-24 Revised:2014-02-07 Online:2014-07-28 Published:2014-08-23
Supported by:
Supported by theNational Natural Science Foundation of China (61374140), Shanghai Postdoctoral Sustentation Fund (12R21412600), the Fundamental Research Funds for the Central Universities (WH1214039), and Shanghai Pujiang Program (12PJ1402200).

摘要/Abstract

摘要： Complex industrial processes often havemultiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient moving windowlocal outlier probability algorithmis proposed. Its key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probabilitymodels, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

关键词: Time-varying, Complex data distribution, Local outlier probability, Multi-mode, Fault detection

Abstract: Complex industrial processes often havemultiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient moving windowlocal outlier probability algorithmis proposed. Its key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probabilitymodels, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

Key words: Time-varying, Complex data distribution, Local outlier probability, Multi-mode, Fault detection

Yuxin Ma, Hongbo Shi, Mengling Wang. Adaptive Local Outlier Probability for Dynamic Process Monitoring[J]. , 2014, 22(7): 820-827.

参考文献

[1] W.H. Li, H.H. Yue, S.V. Cervantes, S.J. Qin, Recursive PCA for adaptive process monitoring, J. Process Control 10 (2000) 471-486.
[2] C. Rosen, J.A. Lennox, Multivariate and multi-scale monitoring of wastewater treatment operation, Water Res. 35 (2001) 3402-3410.
[3] Y.W. Zhang, S. Li, Y.D. Teng, Dynamic process monitoring using recursive kernel principle component analysis, Chem. Eng. Sci. 72 (2012) 78-86.
[4] X.Q. Liu, U. Kruger, T. Littler, L. Xie, S.Q. Wang, Moving window kernel PCA for adaptive monitoring of nonlinear processes, Chemom. Intell. Lab. Syst. 96 (2009) 132-143.
[5] F.L.Wang, S. Tan, J. Peng, Y.Q. Chang, Process monitoring based on mode identification for multi-mode process with transitions, Chemom. Intell. Lab. Syst. 110 (2012) 144-155.
[6] H.H. Ma, Y. Hu, H.B. Shi, Fault detection and identification based on neighborhood standardized local outlier factor method, Ind. Eng. Chem. Res. 52 (2013) 2389-2402.
[7] P. Teppola, S.P. Mujunen, P. Minkkinen, Adaptive fuzzy C-means clustering in process monitoring, Chemom. Intell. Lab. Syst. 45 (1999) 23-38.
[8] J.L. Liu,Modeling a large-scale nonlinear system using adaptive Takagi-Sugeno fuzzy model on PCA subspace, Ind. Eng. Chem. Res. 46 (2007) 788-800.
[9] M. Petković, M.R. Rapaić, Z.D. Jeličić, A. Pisano, On-line adaptive clustering for process monitoring and fault detection, Expert Syst. Appl. 39 (2012) 10226-10235.
[10] H.D. Jin, Y.H. Lee, G. Lee, C.H. Han, Robust recursive principal component analysis modeling for adaptive monitoring, Ind. Eng. Chem. Res. 45 (2006) 696-703.
[11] Y.H. Lee, H.D. Jin, C.H. Han, On-line process state classification for adaptive monitoring, Ind. Eng. Chem. Res. 45 (2006) 3095-3107.
[12] S.W. Choi, E.B. Martin, A.J. Morris, I.B. Lee, Adaptive multivariate statistical process control for monitoring time-varying processes, Ind. Eng. Chem. Res. 45 (2006) 3108-3118.
[13] Z.Q. Ge, Z.H. Song, Online monitoring of nonlinear multiple mode processes based on adaptive local model approach, Control. Eng. Pract. 16 (2008) 1427-1437.
[14] X. Xie, H.B. Shi, Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models, Ind. Eng. Chem. Res. 51 (2012) 5497-5505.
[15] J. Yu, A particle filter driven dynamic Gaussianmixture model approach for complex process monitoring and fault diagnosis, J. Process Control 22 (2012) 778-788.
[16] M. Kermit, O. Tomic, Independent component analysis applied on gas sensor array measurement data, IEEE Sens. J. 3 (2003) 218-228.
[17] Z.Q. Ge, Z.H. Song, Process monitoring based on independent component analysisprincipal component analysis (ICA-PCA) and similarity factors, Ind. Eng. Chem. Res. 46 (2007) 2054-2063.
[18] C.H. Zhao, F.R. Gao, F.L. Wang, Nonlinear batch process monitoring using phasebased kernel independent component analysis-principal component analysis (KICA-PCA), Ind. Eng. Chem. Res. 48 (2009) 9163-9174.
[19] H.P. Kriegel, P. Kröger, E. Schubert, A. Zimek, LoOP: Local Outlier Probabilities, Proceedings of the 18th ACM conference on Information and knowledge management, 2009, pp. 1649-1652.
[20] J.S. Lee, B.Y. Kang, S.H. Kang, Integrating independent component analysis and local outlier factor for plant-wide process monitoring, J. Process Control 21 (2011) 1011-1021.
[21] J.M. Lee, S.J. Qin, I.B. Lee, Fault detection and diagnosis based on modified independent component analysis, AIChE J. 52 (2006) 3501-3514.
[22] X. Wang, U. Kruger, G.W. Irwin, Process monitoring approach using fast moving window PCA, Ind. Eng. Chem. Res. 44 (2005) 5691-5702.
[23] S. Yoon, J.F. MacGregor, Fault diagnosis with multivariate statistical models part I: using steady state fault signatures, J. Process Control 11 (2001) 387-400.

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Adaptive Local Outlier Probability for Dynamic Process Monitoring

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