Fault detection for batch processes based on Gaussian mixture model

Zidonghua Xuebao/Acta Automatica Sinica

Volumn 41, Issue 5, 2015, Pages 899-905

  Wang, Jing ^a   Hu, Yi ^a   Shi, Hong Bo ^a  

^a School of Information Science and Engineering   (China)

Author keywords

Batch processes; Fault detection; Gaussian mixture model (GMM); Global probability index; Multiple operation phases

Indexed keywords

BATCH DATA PROCESSING; FAULT DETECTION; STATISTICS;

BATCH PROCESS; GAUSSIAN MIXTURE MODEL; LOW-DIMENSIONAL REPRESENTATION; MODIFIED ALGORITHMS; MULTIPLE OPERATIONS; PROBABILITY INDEX; SEMICONDUCTOR PROCESS; STATISTICAL DISTRIBUTION;

GAUSSIAN DISTRIBUTION;

EID: 84930174753     PISSN: 02544156     EISSN: None     Source Type: Journal    
DOI: 10.16383/j.aas.2015.c130680     Document Type: Article

References (17)

1. Cinar A, Parulekar S, Undey C, Birol G. Batch Fermentation: Modeling, Monitoring, and Control. New York: CRC Press, 2003.
2. Lee J M, Yoo C K, Lee I B. Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. Journal of Biotechnology, 2004, 110(2): 119-136
3. Nomikos P, MacGregor J F. Monitoring batch processes using multi-way principal component analysis. AIChE Journal, 1994, 40(8): 1361-1375
4. Kourti T, Nomikos P, MacGregor J F. Analysis, monitoring and fault diagnosis of batch processes using multi-block and multiway PLS. Journal of Process Control, 1995, 5(4): 277-284
5. Zhang Jia, Sun Wei, Zhao Jin-Song, Sun Mei-Hong. Fault detection of batch process based on multi-phase multi-way principal component analysis. Computers and Applied Chemistry, 2010, 27(3): 298-302
6. Chang Yu-Qing, Wang Zhu, Tan Shuai, Wang Fu-Li, Yang Jie. Research on multistage-based MPCA modeling and monitoring method for batch processes. Acta Automatica Sinica, 2010, 36(9): 1312-1320
7. Yu J, Qin S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal, 2008, 54(7): 1811-1829
8. Yu J, Qin S J. Multiway Gaussian mixture model based multiphase batch process monitoring. Industrial and Engineering Chemistry Research, 2009, 48(18): 8585-8594
9. Zhao Chun-Hui, Wang Fu-Li, Yao Yuan, Gao Fu-Rong. Phase-based statistical modeling, online monitoring and quality prediction for batch processes. Acta Automatica Sinica, 2010, 36(3): 366-374
10. Rothwell S G, Martin E B, Morris A J. Comparison of methods for dealing with uneven length batches. In: Proceedings of the 1998 International Conference on Computer Application in Biotechnology. Osaka, Japan: Elsevier, 1998. 387-392
11. Zhou Dong-Hua, Li Gang, Li Yuan. Fault Diagnosis Technology of Data-driven in Industrial Process. Beijing: Science Press, 2011. 39-41
12. He Ming, Feng Bo-Qin, Ma Zhao-Feng, Fu Xiang-Hua. A unsupervised rough clustering method based on Gaussian mixture model. Journal of Harbin Institute of Technology, 2006, 38(2): 256-259, 322
13. Figueiredo M A T, Jain A K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(3): 381-396
14. Choi S W, Park J H, Lee I B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Computers and Chemical Engineering, 2004, 28(8): 1377-1387
15. Chiang L H, Russell E, Braatz R D. Fault Detection and Diagnosis in Industrial Systems. New York: Springer Verlag, 2001. 36-37
16. Xie X, Shi H B. Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models. Industrial and Engineering Chemistry Research, 2012, 51(15): 5497-5505
17. Barry M W, Neal B G, Stephanie W B, Daniel D W J, Gabriel G B. A comparison of principal component analysis, multiway principal component analysis, trilinear decom-position and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics, 1999, 13(3-4): 379-396