Multi-mode process monitoring method based on PCA mixture model

Huagong Xuebao/CIESC Journal

Volumn 62, Issue 3, 2011, Pages 743-752

  Xu, Xianzhen ^a   Xie, Lei ^a   Wang, Shuqing ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Fault detection; Mixture model; Multiple mode; PCA; Statistical monitoring

Indexed keywords

DETECTION METHODS; EARLY DETECTION; EXPECTATION MAXIMIZATION; INCREMENTAL EM; MIXTURE COMPONENTS; MIXTURE MODEL; MODEL SELECTION; MONITORING APPROACH; MULTIMODES; MULTIPLE MODE; NONSINGULAR; NUMERICAL EXAMPLE; OPERATING CONDITION; PCA; STATISTICAL MONITORING; TE PROCESS;

ALGORITHMS; COVARIANCE MATRIX; MIXTURES; MULTIVARIANT ANALYSIS; OPTIMIZATION; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; PROCESS MONITORING;

FAULT DETECTION;

EID: 79955411528     PISSN: 04381157     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Chiang L H, Ressell E L, Braatz R D. Fault Detection and Diagnosis in Industrial System[M]. London: Springer-Verlag, 2001
2. Zullo L. Validation and verification of continuous plants operating modes using multivariate statistical methods[J]. Computers & Chemical Engineering, 1996, 20: 683-688
3. Hwang D H, Han C. Real-time monitoring for a process with multiple operating modes[J]. Control Engineering Practice, 1999, 7: 891-902
4. Zhao S J, et al. A multiple PCA model based technique for the monitoring of processes with multiple operating modes//A Barbosa-povoa, European Symposium on Computer Aided Chemical Engineering-14[C]. Lisbon, Portugal: Elsevier, 2004: 865-870
5. Niu Zheng, Liu Jizhen, Niu Yuguang. Fault detection under varying load conditions based on dynamic multi-principal component models[J]. Journal of Power Engineering, 2005, 25(4): 554-558
6. Choi S W, Park J H, Lee I B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis[J]. Computers & Chemical Engineering, 2004, 28(8): 1377-1387
7. Yu J, Qin S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models[J]. AIChE J., 2008, 54(7): 1811-1829
8. Kim H C, Kim D, Bang S Y. An efficient model order selection for PCA mixture model[J]. Pattern Recognition Letters, 2003, 24(9/10): 1385-1393
9. Li Lei, Ma Jinwen. A BYY scale-incremental EM algorithm for Gaussian mixture learning[J]. Applied Mathematics and Computation, 2008, 205(2): 832-840
10. Li J Q, Barron A R. Mixture density estimation[J]. Advances in Neural Information Processing System, 2000, 12: 279-285
11. Vlassis N, Likas A. A greedy EM algorithm for Gaussian mixture learning[J]. Neural Processing Letters, 2002, 15: 77-87
12. Verbeek J J, Vlassis N, Krose B. Efficient greedy learning of Gaussian mxiture models[J]. Neural Computation, 2003, 15(2): 469-485
13. Ma Jinwen, Wang T, Xu L. A gradient BYY harmony learning rule on Gaussian mixture with automated model selection[J]. Neurocomputing, 2004, 56: 481-487
14. Ma Jinwen. Automated model selection(AMS) on finite mixtures: a theoretical analysis//Proceedings of the 2006 International Joint Conference on Neural Networks[C]. Vancouver BC, Canada: 8255-8261
15. Downs J J, Vogel E F. Plant-wide industrial process control problem[J]. Computers & Chemical Engineering, 1993, 17: 245-255
16. Ricker N L. Decentralized control of the Tennessee Eastman challenge process[J]. Journal of Process Control, 1996, 6: 205-221