A fault magnitude based strategy for effective fault classification

Chemical Engineering Research and Design

Volumn 91, Issue 3, 2013, Pages 530-541

  Lee, Chang Jun ^a   Lee, Gibaek ^b   Lee, Jong Min ^c  

^a Pukyong National University   (South Korea)

^b Chungju National University   (South Korea)

^c Seoul National University   (South Korea)

Author keywords

DAMADICS; Principal component analysis; Support vector machine; System decomposition

Indexed keywords

BENCH-MARK PROBLEMS; CONVENTIONAL APPROACH; DAMADICS; EMPIRICAL MODEL; FAULT CLASSIFICATION; FAULT MAGNITUDES; MULTIPLE FAULTS; NORMAL OPERATIONS; PREDICTIVE VARIABLES; PRINCIPAL COMPONENTS; PROCESS VARIABLES; ROOT CAUSE; SUPPORT VECTOR REGRESSION (SVR); SYSTEM DECOMPOSITION;

PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

FAULT DETECTION;

EID: 84874279814     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cherd.2012.09.014     Document Type: Article

References (38)

1. Akaike H. A new look at the statistical model identification. IEEE Trans. Automat. Control 1974, 19:716-723.
2. BartyŚ M., Patton R., Syfert M., de las Heras S., Quevedo J. Introduction to the DAMADICS actuator FDI benchmark study. Control Eng. Pract. 2006, 14:577-596.
3. Bloch G., Ouladsine M., Thomas P. On-line fault diagnosis of dynamic systems via robust parameter estimation. Control Eng. Pract. 1995, 3:1709-1717.
4. Chiang L.H., Braatz R.D. Process monitoring using casual map and multivariate statistics: fault detection and identification. Chemometr. Intell. Lab. Syst. 2003, 65:159-178.
5. Chiang L.H., Russell E.L., Braatz R.D. Fault Detection and Diagnosis in Industrial Systems 2001, Springer-Verlag, London.
6. Choi S.W., Park J.H., Lee I.B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Comput. Chem. Eng. 2004, 28:1377-1387.
7. Deshpande A.P., Patwardhan S.C., Narasimhan S.S. Intelligent state estimation for fault tolerant nonlinear predictive control. J. Process Control 2009, 19:187-204.
8. Detroja K.P., Gudi R.D., Patwardhan S.C. A possibilistic clustering approach to novel fault detection and isolation. J. Process Control 2006, 16:1055-1073.
9. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2001, Springer, New York.
10. Iri M., Aoki K., O'Shima E. An algorithm for diagnosis of system failures in the chemical process. Comput. Chem. Eng. 1979, 3:489-493.
11. Kano M., Tanaka S., Hasebe S., Hashimoto I., Ohno H. Monitoring independent components for fault detection. AIChE J. 2003, 49:969-976.
12. KoŚcielny J.M., BartyŚ M., Rzepiejewski P., da Costa J.S. Actuator fault distinguishability study for the DAMADICS benchmark problem. Control Eng. Pract. 2006, 14:645-652.
13. Kramer M., Palowitch B. Rule-based approach to fault diagnosis using the signed directed graph. AIChE J. 1987, 33:1067-1078.
14. Lee G., Han C., Yoon E.S. Multiple-fault diagnosis of the Tennessee Eastman process based on system decomposition and dynamics PLS. Ind. Eng. Chem. Res. 2004, 43:8037-8048.
15. Lee G., Song S.-O., Yoon E.S. Multiple-fault diagnosis based on system decomposition and dynamic PLS. Ind. Eng. Chem. Res. 2003, 42:6145-6154.
16. Lee J.M., Lee J.H. Approximate dynamic programming based approaches for input-output data-driven control of nonlinear processes. Automatica 2005, 41:1281-1288.
17. Manders E.J., Narasimhan S., Biswas G., Mosterman P.J. A combined qualitative/quantitative approach for fault isolation in continuous dynamic systems. Proceedings of the 4th Symposium on Fault Detection, Supervision and Safety for Technical Processes 2000, 1074-1079. Elsevier, Budapest.
18. Maurya M.R., Rengaswamy R., Venkatasubramanian V. Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheet. Eng. Appl. Artif. Intell. 2004, 17:501-518.
19. Montgomery D.C. Introduction to Statistical Quality Control 2001, John Wiley, New York.
20. Nandi S., Badhe Y., Lonari J., Sridev U., Rao B.S., Tambe S.S., Kulkarni B.D. Hybrid process modelling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on hbeta catalyst. Chem. Eng. J. 2004, 97:115-129.
21. Narasimhan S., Mosterman P.J., Biswas G. A systematic analysis of measurement selection algorithms for fault isolation in dynamic systems. Proceedings of the 9th International Workshop on Principles of Diagnosis 1998, 94-101.
22. Prakash J., Narasimhan S., Patwardhan S.C. Integrating model based fault diagnosis with model predictive control. Ind. Eng. Chem. Res. 2005, 44:4344-4360.
23. Prakash J., Patwardhan S.C., Narasimhan S. A supervisory approach to fault tolerant control of linear multivariable systems. Ind. Eng. Chem. Res. 2002, 41:2270-2281.
24. Rhodes C., Morari M. Determining the model order of nonlinear input/output systems. AIChE J. 1998, 44:151-163.
25. Rodrigues M., Theilliol D., Adam-Medina M., Sauter D. A fault detection and isolation scheme for industrial systems based on multiple operating models. Control Eng. Pract. 2008, 16:225-239.
26. Sa da Costa J., Louro R. Modelling and simulation of an industrial actuator valve for fault diagnosis benchmark. Proceedings of the 4th International Symposium on Mathematical Modeling 2003, 1212-1221.
27. Schölkopf B., Smola A., Müller K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998, 5:1299-1319.
28. Umeda T., Kuriyama T., O'Shima E., Matsuyama H. A graphical approach to cause and effect analysis of chemical processing systems. Chem. Eng. Sci. 1980, 35:2379-2388.
29. Vapnik V.N. The Nature of Statistical Learning Theory 1995, John Wiley & Sons, New York.
30. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N. A review of process fault detection and diagnosis. Part I: Quantitative model-based methods. Comput. Chem. Eng. 2003, 27:293-311.
31. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N. A review of process fault detection and diagnosis. Part II: Qualitative models and search strategies. Comput. Chem. Eng. 2003, 27:313-326.
32. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N. A review of process fault detection and diagnosis. Part III: Process history based methods. Comput. Chem. Eng. 2003, 27:327-346.
33. Westerhius J.A., Gurden S.P., Smilde A.K. Standardized Q-static for improved sensitivity in the monitoring of residuals in MSPC. J. Chemometr. 2000, 14:335-349.
34. Wilcox N., Himmelblau D.M. The possible cause-effect graphs (PSEG) model for fault diagnosis - I. Methodology. Comput. Chem. Eng. 1994, 18:103-116.
35. Wong J.C., McDonald K.A., Palazoglu A. Classification of abnormal plant operation using multiple process variable trends. J. Process Control 2001, 11:409-418.
36. Wang L., Shi H. Multivariate statistical process monitoring using an improved independent component analysis. Chem. Eng. Res. Des. 2010, 88:403-414.
37. Yu J., Qin S.J. Multiway Gaussian mixture model based multiphase batch process monitoring. Ind. Eng. Chem. Res. 2008, 48:8585-8594.
38. Zhang X., Hoo K.A. Effective fault detection and isolation using bond graph-based domain decomposition. Comput. Chem. Eng. 2011, 35:132-148.