A semi-supervised approach to fault diagnosis for chemical processes

Computers and Chemical Engineering

Volumn 34, Issue 5, 2010, Pages 631-642

  Monroy, Isaac ^a   Benitez, Raul ^a   Escudero, Gerard ^a   Graells, Moisès ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

BIC; Fault diagnosis; GMM; ICA; SVM; Tennessee Eastman

Indexed keywords

BAYESIAN INFORMATION CRITERION; CHEMICAL PROCESS; DIAGNOSIS PERFORMANCE; FAULT DIAGNOSIS; FAULT DIAGNOSIS SYSTEMS; FEATURE EXTRACTION METHODS; GAUSSIAN MIXTURE MODEL; SEMI-SUPERVISED; TENNESSEE EASTMAN; TENNESSEE EASTMAN PROCESS; UNSUPERVISED CLUSTERING; UNSUPERVISED LEARNING METHOD;

BLIND SOURCE SEPARATION; FAILURE ANALYSIS; FEATURE EXTRACTION; HEMODYNAMICS; INDUSTRIAL APPLICATIONS; LEARNING ALGORITHMS; MAGNETOSTRICTIVE DEVICES; SUPPORT VECTOR MACHINES; UNSUPERVISED LEARNING;

INDEPENDENT COMPONENT ANALYSIS;

EID: 77950367567     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2009.12.008     Document Type: Article

References (39)

1. Benitez R., and Nenadic Z. Robust unsupervised detection of action potentials with probabilistic methods. IEEE Transactions on Biomedical Engineering 55 4 (2008) 1344-1354
2. Bin He X., Pu Yang Y., and Hong Yang Y. Fault diagnosis based on variable-weighted kernel Fisher discriminant analysis. Chemometrics and Intelligent Laboratory Systems 93 (2008) 27-33
3. Bin He X., Wang W., Pu Yang Y., and Hong Y. Variable-weighted Fisher discriminant analysis for process fault diagnosis. Journal of Process Control 19 6 (2009) 923-931
4. Boser B., Guyon I., and Vapnik V. A training algorithm for optimal margin classifiers. Proceedings of the Workshop on Computational Learning Theory (1992), COLT, Pittsburgh, PA
5. In: Chapelle O., Schölkopf B., and Zien A. (Eds). Semi-supervised learning (2006), The MIT Press
6. Chiang L., and Braatz R. Process monitoring using causal map and multivariate statistics: Fault detection and identification. Chemometrics and Intelligent Laboratory Systems 65 (2003) 159-178
7. Chiang L., Russell E., and Braatz R. Fault diagnosis in chemical process using Fisher discriminant analysis, discriminant partial least squares and principal component analysis. Chemometrics and Intelligent Laboratory Systems 50 (2000) 243-252
8. Chiang L., Kotanchek M., and Kordon A. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and Chemical Engineering 28 (2004) 1389-1401
9. Detroja K.P., Gudi R.D., and Patwardhan S.C. Plant-wide detection and diagnosis using correspondence analysis. Control Engineering Practice 15 12 (2007) 1468-1483
10. Downs J.J., and Vogel E.F. A plant wide industrial process control problem. Computers and Chemical Engineering 17 3 (1992) 245-255
11. Fraley C., and Raftery A.E. How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal 41 8 (1998) 578-588
12. Ge Z., Yang C., and Song Z. Improved kernel PCA-based monitoring approach for nonlinear processes. Chemical Engineering Science 64 (2009) 2245-2255
13. Huang C.-L., Liao H.-C., and Chen M.-C. Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Systems with Applications 34 (2008) 578-587
14. Jakubek S.M., and Strasser T.I. Artificial neural networks for fault detection in large-scale data acquisition systems. Engineering Applications of Artificial Intelligence 17 (2004) 233-248
15. Jiang L., and Wang S. Fault diagnosis based on Independent Component Analysis and Fisher Discriminant Analysis. Proceedings of the third conference on machine learning and cybernetics. Shanghai, August 26-29 (2004)
16. Joachims T. Making large-scale SVM learning practical. In: Schölkopf B., Burges C., and Smola A. (Eds). Advances in Kernel methods-support vector learning (1999), MIT Press
17. Kano M., Nagao M., Hasebe S., Hashimoto I., Ohno H., Strauss R., et al. Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem. Computers and Chemical Engineering 26 (2002) 161-174
18. Kano Manabu, Tanaka S., Hasebe S., Hashimoto I., and Ohno H. Monitoring Independent Components for Fault Detection. AIChE Journal 49 4 (2003) 969-976
19. Kent A., Berry M.M., Luehrs F.U., and Perry J.W. Machine literature searching: VIII. Operational criteria for designing information retrieval systems. American Documentation 6 2 (1955) 93-101
20. Ku W., Storer R., and Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 30 (1995) 179-196
21. Kulkarni A., Jayaraman V.K., and Kulkarni B.D. Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process. Computers & Chemical Engineering 29 (2005) 2128-2133
22. Lee J., Yoo C., and Lee I. Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chemical Engineering Science 59 (2004) 2995-3006
23. Lee J., Qin S.J., and Lee I. Fault detection and diagnosis based on modified independent component analysis. AIChE Journal 52 10 (2006) 3501-3514
24. Li R.F., and Wang X.Z. Dimension reduction of process dynamic trends using independent component analysis. Computers and Chemical Engineering 26 (2002) 467-473
25. Lin W., Qian Y., and Li X. Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis. Computers and Chemical Engineering 24 (2000) 423-429
26. Manning C., and Schütze H. Foundations of statistical natural language processing (1999), The MIT Press
27. Polat K., and Günes S. A novel approach to estimation of E. coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVM). Applied Mathematics and Computation 190 (2007) 1574-1582
28. Russell E., Chiang L., and Braatz R. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 51 (2000) 81-93
29. Schwarz G. Estimating the dimension of a model. Annals of Statistics 6 2 (1978) 461-464
30. Shao J.-D., Rong G., and Lee J. Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis. Chemometrics and Intellingent Laboratory Systems 96 (2009) 75-83
31. Shawe-Taylor J., and Cristianini N. Kernel methods for pattern analysis (2004), Cambridge University Press
32. Tamura M., and Tsujita S. A study on the number of principal components and sensitivity of fault detection using PCA. Computers and Chemical Engineering 31 (2007) 1035-1046
33. Van Rijsbergen C.J. Information retrieval. 2nd ed. (1979), Butterworths, London
34. Venkatasubramanian V., Rengaswamy R., Yin K., and Kavuri S.N. A review of process fault detection and diagnosis: Part I. Quantitative model-based methods. Computers and Chemical Engineering 27 (2003) 293-312
35. Verron S., Tiplica T., and Kobi A. Fault detection and identification with a new feature selection based on mutual information. Journal of Process Control 18 (2008) 479-490
36. Yélamos I., Graells M., Puigjaner L., and Escudero G. Simultaneous fault diagnosis in chemical plants using a MultiLabel approach. AIChE Journal 53 11 (2007) 2871-2884
37. Yélamos I., Escudero G., Graells M., and Puigjaner L. Performance assessment of a novel fault diagnosis system based on support vector machines. Computers and Chemical Engineering 33 (2009) 244-255
38. Yong M., Zheng X., Zheng Y., Youxian S., and Zheng W. Fault diagnosis based on fuzzy support vector machine with parameter tuning and feature selection. Chinese Journal of Chemical Engineering 15 2 (2007) 233-239
39. Zhang Y. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM. Chemical Engineering Science 64 (2009) 801-811