Improved fault diagnosis in multivariate systems using regression-based reconstruction

Control Engineering Practice

Volumn 17, Issue 4, 2009, Pages 478-493

  Lieftucht, Dirk ^a   Völker, Marten ^b   Sonntag, Christian ^b   Kruger, Uwe ^c   Irwin, George W ^d   Engell, Sebastian ^b  

^a SMS DEMAG AG   (Germany)

^b TU DORTMUND UNIVERSITY   (Germany)

^c KHALIFA UNIVERSITY   (United Arab Emirates)

^d QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

Data compression; Dynamic multivariable systems; Fault diagnosis; Industrial plants; Statistical process control

Indexed keywords

CONDITION MONITORING; DATA COMPRESSION; DISTILLATION; DISTILLATION EQUIPMENT; ELECTRIC FAULT LOCATION; IDENTIFICATION (CONTROL SYSTEMS); INDUSTRIAL PLANTS; MULTIVARIABLE SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL; QUALITY CONTROL; RESTORATION; SURFACE TREATMENT;

DYNAMIC MULTIVARIABLE SYSTEMS; DYNAMIC PRINCIPAL COMPONENT ANALYSIS; EXPERIMENTAL DATUM; FAULT CONDITIONS; FAULT DIAGNOSIS; FAULT IDENTIFICATIONS; FAULT SCENARIOS; FAULT SIGNATURES; INHERENT LIMITATIONS; MONITORING TOOLS; MULTIVARIATE SYSTEMS; REACTIVE DISTILLATIONS; RECONSTRUCTION TECHNIQUES; SIMULATION EXAMPLES;

STATISTICAL PROCESS CONTROL;

EID: 60249085725     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2008.09.009     Document Type: Article

References (43)

1. Agreda V.H., Partin L.R., and Heise W.H. High purity methyl acetate via reactive distillation. Chemical Engineering Progress 86 2 (1990) 40-46
2. Aguado D., Ferrer A., Ferrer J., and Seco A. Singular value decomposition-based ma order determination of non-Gaussian arma models. IEEE Transactions on Signal Processes 41 8 (1993) 2657-2664
3. AlGhazzawi A., and Lennox B. Monitoring a complex refining process using multivariate statistics. Control Engineering Practice 16 (2008) 294-307
4. Arteaga F., and Ferrer A. Dealing with missing data in mspc: Several methods, different interpretations, some examples. Journal of Chemometrics 16 (2002) 408-418
5. Broomhead D.S., and Lowe D. Multivariable functional interpolation and adaptive networks. Complex Systems 2 (1988) 312-355
6. Dunia R., and Qin S.J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal 44 8 (1998) 1813-1831
7. Dunia R., and Qin S.J. A unified geometric approach to process and sensor fault identification and reconstruction: The unidimensional fault case. Computers & Chemical Engineering 22 7-8 (1998) 927-943
8. Dunia R., Qin S.J., Edgar T.F., and McAvoy T.J. Identification of faulty sensors using principal component analysis. AIChE Journal 10 10 (1996) 2797-2812
9. Foxboro, L. (1998). Foxboro MRA-Connoisseur. User manual.
10. Jackson J.E. Principal components and factor analysis: Part i: Principal components. Journal of Quality Control 12 4 (1980) 201-213
11. Jackson J.E. A users guide to principal components. Wiley series in probability and mathematical statistics (1991), Wiley, New York
12. Jackson J.E., and Mudholkar G.S. Control procedures for residuals associated with principal component analysis. Technometrics 21 (1979) 341-349
13. Kourti T. Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing 19 (2005) 213-246
14. Kourti T., Lee J., and MacGregor J.F. Experiences with industrial applications of projection methods for multivariate statistical process control. Computers & Chemical Engineering 20 971 (1996) 745-750
15. Kresta J.V., MacGregor J.F., and Marlin T.E. Multivariate statistical monitoring of process operating performance. The Canadian Journal of Chemical Engineering 69 (1991) 35-47
16. Ku W., Storer R.H., and Georgakis C. Disturbance rejection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 30 (1995) 179-196
17. Li, P., Treasure, R. J., & Kruger, U. (2005). Dynamic principal component analysis using subspace model identification. In Advances in intelligent computing, advances in intelligent computing. Lecture notes in computer science. Springer, Berlin, Heidelberg, pp. 727-736.
18. Li W., and Shah S. Structured residual vector-based approach to sensor faults detection and isolation. Journal of Process Control 12 3 (2002) 429-443
19. Li W., and Shah S. Subspace identification of continuous time models for process fault detection and isolation. Journal of Process Control 13 5 (2002) 407-421
20. Lieftucht, D., Kruger, U., Irwin, G. (2004). Improved diagnosis of sensor faults using multivariate statistics. In Proceedings of the American control conference. Boston, Massachusetts, USA, pp. 4403-4407.
21. Lieftucht, D., Kruger, U., & Irwin, G. (2006a). Regression-based variable reconstruction in multivariate systems. In Proceedings of the 6th IFAC symposium on fault detection, supervision and safety of technical processes (Safeprocess 2006), volume. Beijing, PR China, pp. 433-438.
22. Lieftucht D., Kruger U., and Irwin G.W. Improved diagnosis of abnormal process behaviour using multivariate statistics. Computers & Chemical Engineering 30 5 (2006) 901-912
23. Lieftucht D., Kruger U., Irwin G.W., and Treasure J.R. Fault reconstruction in linear dynamic systems using multivariate statistics. IET Control Theory & Applications 153 4 (2006) 437-446
24. MacGregor J.F., and Kourti T. Statistical process control of multivariate processes. Control Engineering Practice 3 3 (1995) 403-414
25. MacGregor, J. F., Marlin, T. E., Kresta, J. V., & Skagerberg, B. (1991). Multivariate statistical methods in process analysis and control. In Proceedings of the 4th international conference on chemical process control (pp. 79-99). AIChE Publication, No. P-67. New York.
26. Martin E.B., and Morris A.J. An overview of multivariate statistical process control in continuous and batch process performance monitoring. Transactions of the Institute of Measurement and Control 18 1 (1996) 51-60
27. Miller P., Swanson R.E., and Heckler C.F. Contribution plots: A missing link in multivariate quality control. Applied Mathematics and Computer Science 8 4 (1998) 775-792
28. Moody J., and Darken C.J. Fast learning in networks of locally-tuned processing units. Neural Computation 1 (1989) 151-160
29. Nelson P.R.C., Taylor P.A., and MacGregor J.F. Missing data methods in pca and pls: Score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems 35 1 (1996) 45-65
30. Noeres C., Kenig E.Y., and Gorak A. Modelling of reactive separation processes: Reactive absorption and reactive distillation. Chemical Engineering and Processing 42 (2003) 157-178
31. Qin S. Statistical process monitoring: Basics and beyond. Journal of Chemometrics 17 (2003) 480-502
32. Raich A., and Cinar A. Diagnosis of process disturbances by statistical distance and angel measures. Computers & Chemical Engineering 21 6 (1997) 661-673
33. Renals, S., & Rohwer, R. (1989). Phoneme classification experiments using radial basis functions. In Proceedings of the international joint conference on neural networks (pp. 461-467). Washington, DC.
34. Rodrigues M., Theilliol D., Adam-Medina M., and Sauter D. A fault detection and isolation scheme for industrial systems based on multiple operating models. Control Engineering Practice 16 2 (2008) 225-239
35. Russell, E. L., Chiang, L. H., & Braatz, R. D. (2000). Data-driven techniques for fault detection and diagnosis in chemical processes. In Advances in industrial control. London: Springer.
36. Sonntag, C. (2004). Control-relevant identification of a reactive distillation column. Master's thesis, University of Dortmund, Process Control Laboratory.
37. Treasure R., Kruger U., and Cooper J. Dynamic multivariable statistical process control using subspace identification. Journal of Process Control 14 3 (2004) 279-292
38. van Overschee P., and de Moor B. System identification for the identification of combined deterministic-stochastic systems. Automatica 30 1 (1994) 75-93
39. Voelker, M., Sonntag, C., & Engell, S. (2005). Design of robust low-order controllers for complex processes: A case study on reactive distillation in a medium-scale pilot plant. In Proceedings of the 16th IFAC World Congress Prague.
40. Voelker M., Sonntag C., and Engell S. Control of integrated processes: A case study on reactive distillation in a medium-scale pilot plant. Control Engineering Practice 15 7 (2007) 863-881
41. Walczak B., and Massart D.L. The radial basis functions-Partial least squares approach as a flexible non-linear regression technique. Analytica Chimica Acta 331 (1996) 177-185
42. Wise B.M., and Gallagher N.B. The process chemometrics approach to process monitoring and fault detection. Journal of Process Control 6 6 (1996) 329-348
43. Yao S.C., and Zafiriou E. Control system sensor failure detection via networks of localized receptive fields (1990), American Control Conference, San Diego, CA