Process monitoring using causal map and multivariate statistics: Fault detection and identification

Chemometrics and Intelligent Laboratory Systems

Volumn 65, Issue 2, 2003, Pages 159-178

  Chiang, Leo H ^a   Braatz, Richard D ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Causal map; Chemometrics methods; Fault detection; Fault identification; Multivariate statistics; Process monitoring

Indexed keywords

ANALYSIS OF VARIANCE; ARTICLE; COVARIANCE; DATA ANALYSIS; DIAGNOSTIC ERROR; ENGINEERING; INFORMATION; METHODOLOGY; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS MONITORING; REGRESSION ANALYSIS;

EID: 0037470096     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-7439(02)00140-5     Document Type: Article

References (41)

1. Chiang L.H., Russell E.L., Braatz R.D. Fault Detection and Diagnosis in Industrial Systems. 2001;Springer Verlag, London.
2. Russell E.L., Chiang L.H., Braatz R.D. Data-driven Techniques for Fault Detection and Diagnosis in Chemical Processes. 2000;Springer Verlag, London.
3. Russell E.L., Chiang L.H., Braatz R.D. Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems. 51:2000;81-93.
4. Dunia R., Qin S.J. Joint diagnosis of process and sensor faults using principal component analysis. Control Engineering Practice. 6:1998;457-469.
5. Kourti T., MacGregor J.F. Multivariate SPC methods for process and product monitoring. Journal of Quality Technology. 28:1996;409-428.
6. Bakshi B.R. Multiscale PCA with application to multivariate statistical process monitoring. AIChE Journal. 44:1998;1596-1610.
7. Raich A.C., Cinar A. Statistical process monitoring and disturbance diagnosis in multivariable continuous processes. AIChE Journal. 42:1996;995-1009.
8. Ku W., Storer R.H., Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems. 30:1995;179-196.
9. Iri M., Aoki K., O'Shima E., Matsuyama H. An algorithm for diagnosis of system failures in the chemical process. Computers & Chemical Engineering. 3:1979;489-493.
10. Shiozaki J., Matsuyama H., O'Shima E., Iri M. An improved algorithm for diagnosis of system failures in the chemical process. Computers & Chemical Engineering. 9:1985;285-293.
11. Tsuge Y., Shiozaki J., Matsuyama H., O'Shima E. Fault diagnosis algorithms based on the signed directed graph and its modifications. Industrial and Chemical Engineering Symposium Series. 92:1985;133-144.
12. Gal I.P., Hangos K.M. SDG model-based structures for fault detection. Proc. of the IFAC On-line Fault Detection and Supervision in the Chemical Process Industries, Piscataway, NJ. 1998;243-248 IEEE Press.
13. Allen D.J. Digraphs and fault trees. Industrial & Engineering Chemistry Fundamentals. 23:1984;175-180.
14. Lapp S.A., Power G.J. Computer-aided synthesis of fault tree. IEEE Transactions on Reliability. 28:1977;2-13.
15. Schneeweiss W.G. Fault tree analysis in case of multiple faults, especially covered and uncovered ones. Microelectronics and Reliability. 38:1998;659-663.
16. Petti T.F., Klein J., Dhurjati P.S. Diagnostic model processor. Using deep knowledge for process fault diagnosis. AIChE Journal. 36:1990;565-575.
17. Zhao J., Chen B., Shen J. A hybrid ANN-ES system for dynamic fault diagnosis of hydrocracking process. Computers & Chemical Engineering. 21:1997;S929-S933.
18. Doyle R.J. Determining the loci of anomalies using minimal causal models. International Joint Conference on Artificial Intelligence. 1995 (August);1a-7a American Association for Artificial Intelligence, Montreal, Canada.
19. Doyle R.J., Charest L., Rouquette N., Wyatt J., Robertson C. Causal modeling and event-driven simulation for monitoring of continuous systems. Computers in Aerospace. 9:1993;395-405.
20. Doyle R.J., Chien S.A., Fayyad U.M., Wyatt E.J. Focused real-time systems monitoring based on multiple anomaly models. 7th Int. Workshop on Qualitative Reasoning, Eastsound, Washington, May:1993.
21. E.L. Russell. Process Monitoring of Large Scale Systems. PhD thesis, University of Illinois, Urbana, IL, 1998.
22. L.H. Chiang. Fault Detection and Diagnosis for Large-Scale Systems. PhD thesis, University of Illinois, Urbana, IL, 2001.
23. Ljung L. System Identification: Theory for the User. 1987;Prentice-Hall, Englewood Cliffs, NJ.
24. Fasolo P., Seborg D.E. SQC approach to monitoring and fault detection in HVAC control systems. Proc. of the American Control Conf., Piscataway, NJ. vol. 3:1994;3055-3059 IEEE Press.
25. Montgomery D.C. Introduction to Statistical Quality Control. 1985;Wiley, New York.
26. Ogunnaike B.A., Ray W.H. Process Dynamics, Modeling, and Control. 1994;Oxford Univ. Press, New York.
27. MacGregor J.F., Kourti T. Statistical process control of multivariate processes. Control Engineering Practice. 3:1995;403-414.
28. Downs J.J., Vogel E.F. A plant-wide industrial-process control problem. Computers & Chemical Engineering. 17:1993;245-255.
29. Chiang L.H., Russell E.L., Braatz R.D. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometrics and Intelligent Laboratory Systems. 50:2000;243-252.
30. Raich A.C., Cinar A. Multivariate statistical methods for monitoring continuous processes: assessment of discriminatory power disturbance models and diagnosis of multiple disturbances. Chemometrics and Intelligent Laboratory Systems. 30:1995;37-48.
31. Raich A.C., Cinar A. Process disturbance diagnosis by statistical distance and angle measures. Proc. of the 13th IFAC World Congress, Piscataway, NJ. 1996;283-288 IEEE Press.
32. Aradhye H.B., Bakshi B.R., Strauss R. Process monitoring by PCA, dynamic PCA, and multiscale PCA - Theoretical analysis and disturbance detection in the Tennessee Eastman process. AIChE Annual Meeting:1999;. Paper 224g.
33. Chen G., McAvoy T.J. Predictive on-line monitoring of continuous processes. Journal of Process Control. 8:1997;409-420.
34. Cho H., Han C. Hierarchical plant-wide monitoring and triangular representation based diagnosis. Proc. of the IFAC On-line Fault Detection and Supervision in the Chemical Process Industries, Piscataway, NJ. 1998;29-34 IEEE Press.
35. Georgakis C., Steadman B., Liotta V. Decentralized PCA charts for performance assessment of plant-wide control structures. Proc. of the 13th IFAC World Congress, Piscataway, NJ. 1996;97-101 IEEE Press.
36. Gertler J., Li W., Huang Y., McAvoy T. Isolation enhanced principal component analysis. AIChE Journal. 45:1999;323-334.
37. Kano M., Nagao K., Hasebe S., Hashimoto I., Ohno H., Strauss R., Bakshi B. Comparison of statistical process monitoring methods: application to the Eastman challenge problem. Computers & Chemical Engineering. 24:2000;755-760.
38. Himes D.M., Storer R.H., Georgakis C. Determination of the number of principal components for disturbance detection and isolation. Proc. of the American Control Conf., Piscataway, NJ. 1994;1279-1283 IEEE Press.
39. W.E. Larimore, D.E. Seborg. Short Course: Process Monitoring and Identification of Dynamic Systems Using Statistical Techniques, Los Angeles, 1997.
40. Lin W., Qian Y., Li X. Nonlinear dynamic principal component analysis for on-line process monitoring and diagnosis. Computers & Chemical Engineering. 24:2000;423-429.
41. Lyman P.R., Georgakis C. Plant-wide control of the Tennessee Eastman problem. Computers & Chemical Engineering. 19:1995;321-331.