Multimodal process monitoring based on transition-constrained Gaussian mixture model

doi:10.1016/j.cjche.2020.08.021

Chinese Journal of Chemical Engineering ›› 2020, Vol. 28 ›› Issue (12): 3070-3078.DOI: 10.1016/j.cjche.2020.08.021

• Process Systems Engineering and Process Safety • Previous Articles Next Articles

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Shutian Chen, Qingchao Jiang, Xuefeng Yan

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:2020-02-24 Revised:2020-07-05 Online:2021-01-11 Published:2020-12-28
Contact: Qingchao Jiang
Supported by:
This work was supported in part by National Natural Science Foundation of China under Grants 61973119 and 61603138, in part by Shanghai Rising-Star Program under Grant 20QA1402600, in part by the Open Funding from Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems under Grant SKDN202001, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Shutian Chen, Qingchao Jiang, Xuefeng Yan

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

通讯作者: Qingchao Jiang
基金资助:
This work was supported in part by National Natural Science Foundation of China under Grants 61973119 and 61603138, in part by Shanghai Rising-Star Program under Grant 20QA1402600, in part by the Open Funding from Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems under Grant SKDN202001, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

Abstract

Abstract: Reliable process monitoring is important for ensuring process safety and product quality. A production process is generally characterized by multiple operation modes, and monitoring these multimodal processes is challenging. Most multimodal monitoring methods rely on the assumption that the modes are independent of each other, which may not be appropriate for practical application. This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring. This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data. This process enables the identified modes to reflect the stability of actual working conditions, improve mode identification accuracy, and enhance monitoring reliability in cases of mode overlap. Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach in multimodal process monitoring with mode overlap.

Key words: Multimodal process monitoring, Gaussian mixture model, State transition matrix, Process control, Process systems, Systems engineering

摘要： Reliable process monitoring is important for ensuring process safety and product quality. A production process is generally characterized by multiple operation modes, and monitoring these multimodal processes is challenging. Most multimodal monitoring methods rely on the assumption that the modes are independent of each other, which may not be appropriate for practical application. This study proposes a transition-constrained Gaussian mixture model method for efficient multimodal process monitoring. This technique can reduce falsely and frequently occurring mode transitions by considering the time series information in the mode identification of historical and online data. This process enables the identified modes to reflect the stability of actual working conditions, improve mode identification accuracy, and enhance monitoring reliability in cases of mode overlap. Case studies on a numerical simulation example and simulation of the penicillin fermentation process are provided to verify the effectiveness of the proposed approach in multimodal process monitoring with mode overlap.

关键词: Multimodal process monitoring, Gaussian mixture model, State transition matrix, Process control, Process systems, Systems engineering

Shutian Chen, Qingchao Jiang, Xuefeng Yan. Multimodal process monitoring based on transition-constrained Gaussian mixture model[J]. Chinese Journal of Chemical Engineering, 2020, 28(12): 3070-3078.

Shutian Chen, Qingchao Jiang, Xuefeng Yan. Multimodal process monitoring based on transition-constrained Gaussian mixture model[J]. 中国化学工程学报, 2020, 28(12): 3070-3078.

References

[1] T. Kourti, J.F. MacGregor, Multivariate SPC methods for process and product monitoring, J.qual.tech 28(1996) 409-428.
[2] Z. Ge, Process data analytics via probabilistic latent variable models:a tutorial review, Ind. Eng. Chem. Res. 57(2018) 12646-12661.
[3] S.J. Qin, Statistical process monitoring:basics and beyond, J. Chemom. 17(2003) 480-502.
[4] J. Feng, J. Wang, H. Zhang, Z. Han, Fault diagnosis method of joint fisher discriminant analysis based on the local and global manifold learning and its kernel version, IEEE Trans. Autom. Sci. Eng. 13(2016) 122-133.
[5] Q. Jiang, X. Yan, B. Huang, Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes, Ind. Eng. Chem. Res. 58(2019) 12899-12912.
[6] D.H. Hwang, C. Han, Real-time monitoring for a process with multiple operating modes, Control. Eng. Pract. 7(1999) 891-902.
[7] S. Lane, E.B. Martin, R. Kooijmans, A.J. Morris, Performance monitoring of a multiproduct semi-batch process, J. Process Control 11(2001) 1-11.
[8] H. Ma, H. Yi, H. Shi, A novel local neighborhood standardization strategy and its application in fault detection of multimode processes, Chemom. Intell. Lab. Syst. 118(2012) 287-300.
[9] C. Tong, A. Palazoglu, X. Yan, An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding, J. Process Control 23(2013) 1497-1507.
[10] Y. Yang, Y. Ma, B. Song, H. Shi, An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes, Chin. J. Chem. Eng. 23(2015) 1357-1363.
[11] S. Wold, Exponentially weighted moving principal components analysis and projections to latent structures, Chemom. Intell. Lab. Syst. 23(1994) 149-161.
[12] H. Dae, Y.H. Lee, G. Lee, C. Han, Robust recursive principal component analysis modeling for adaptive monitoring, Ind. Eng. Chem. Res. 45(2006) 696-703.
[13] 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.
[14] Z. Ge, Z. Song, Online monitoring of nonlinear multiple mode processes based on adaptive local model approach, Control. Eng. Pract. 16(2008) 1427-1437.
[15] M. Kano, S. Hasebe, I. Hashimoto, H. Ohno, Evolution of multivariate statistical process control:application of independent component analysis and external analysis, Comput. Chem. Eng. 28(2004) 1157-1166.
[16] Y.H. Lee, H.D. Jin, C. Han, On-line process state classification for adaptive monitoring, Ind. Eng. Chem. Res. 45(2006) 3095-3107.
[17] K.C. Yoo, K. Villez, I.B. Lee, C. Rosén, P.A. Vanrolleghem, Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor, Biotechnol. Bioeng. 96(2007) 687-701.
[18] J. Liu, Modeling a large-scale nonlinear system using adaptive Takagi-Sugeno fuzzy model on PCA subspace, Ind. Eng. Chem. Res. 46(2007) 788-800.
[19] S.W. Choi, J.H. Park, I.B. Lee, Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis, Comput. Chem. Eng. 28(2004) 1377-1387.
[20] Y. Jie, S.J. Qin, Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models, AIChE J. 54(2008) 1811-1829.
[21] X. Xie, H. Shi, Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models, Ind. Eng. Chem. Res. 51(2012) 5497-5505.
[22] J. Yu, A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes, Chem. Eng. Sci. 68(2012) 506-519.
[23] J. Yu, A new fault diagnosis method of multimode processes using Bayesian inference based Gaussian mixture contribution decomposition, Eng. Appl. Artif. Intell. 26(2013) 456-466.
[24] Q. Jiang, B. Huang, X. Yan, GMM and optimal principal components-based Bayesian method for multimode fault diagnosis, Comput. Chem. Eng. 84(2016) 338-349.
[25] S. Ren, Z. Song, M. Yang, J. Ren, A novel multimode process monitoring method integrating LCGMM with modified LFDA, Chin. J. Chem. Eng. 23(2015) 1970-1980.
[26] W. Sun, A. Palazoğlu, J.A. Romagnoli, Detecting abnormal process trends by waveletdomain hidden Markov models, AIChE J. 49(2003) 140-150.
[27] A. Bakhtazad, A. Palazoglu, J.A. Romagnoli, Detection and classification of abnormal process situations using multidimensional wavelet domain hidden Markov trees, Comput. Chem. Eng. 24(2000) 769-775.
[28] J. Chen, W.J. Chang, Applying wavelet-based hidden Markov tree to enhancing performance of process monitoring, Chem. Eng. Sci. 60(2005) 5129-5143.
[29] Z. Ge, Z. Song, Maximum-likelihood mixture factor analysis model and its application for process monitoring, Chemom. Intell. Lab. Syst. 102(2010) 53-61.
[30] Z. Ge, Z. Song, Mixture Bayesian regularization method of PPCA for multimode process monitoring, AIChE J. 56(2010) 2838-2849.
[31] M. Rashid, J. Yu, Hidden Markov model based adaptive independent component analysis approach for complex chemical process monitoring and fault detection, Ind. Eng. Chem. Res. 51(2012) 5506-5514.
[32] Q. Jiang, X. Yan, B. Huang, Neighborhood variational bayesian multivariate analysis for distributed process monitoring with missing data, IEEE Trans. Control Syst. Technol. 27(6) (2019) 2330-2339.
[33] Q. Jiang, X. Yan, B. Huang, Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference, IEEE Trans. Ind. Electron. 63(2016) 377-386.
[34] Q. Jiang, F. Gao, H. Yi, X. Yan, Multivariate statistical monitoring of key operation units of batch processes based on time-slice CCA, IEEE Trans. Control Syst. Technol. 27(3) (2019) 1368-1375.
[35] M.A.T. Figueiredo, A.K. Jain, Unsupervised learning of finite mixture models, IEEE Trans. Pattern Anal. Mach. Intell. 24(2002) 381-396.
[36] F. Qi, B. Huang, E.C. Tamayo, A Bayesian approach for control loop diagnosis with missing data, AIChE J. 56(2010) 179-195.
[37] G. Birol, C. Ündey, A. Çinar, A modular simulation package for fed-batch fermentation:penicillin production, Comput. Chem. Eng. 26(2002) 1553-1565.
[38] J.M. Lee, C.K. Yoo, I.B. Lee, Statistical process monitoring with independent component analysis, J. Process Control 14(2004) 467-485.
[39] X. Yan, Q. Jiang, Multimode process monitoring using variational Bayesian inference and canonical correlation analysis, IEEE Trans. Autom. Sci. Eng. 16(2019) 1814-1824.

Multimodal process monitoring based on transition-constrained Gaussian mixture model

Multimodal process monitoring based on transition-constrained Gaussian mixture model

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