Root cause analysis in multivariate statistical process monitoring: Integrating reconstruction-based multivariate contribution analysis with fuzzy-signed directed graphs

Computers and Chemical Engineering

Volumn 64, Issue , 2014, Pages 167-177

  He, Bo ^a,c   Chen, Tao ^b   Yang, Xianhui ^a  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF SURREY   (United Kingdom)

^c STATE GRID CORPORATION OF CHINA   (China)

Author keywords

Fault diagnosis; Fuzzy logic; Multivariate statistical process monitoring; Reconstruction based multivariate contributions; Signed directed graph

Indexed keywords

ALGORITHMS; FAILURE ANALYSIS; FUZZY LOGIC; LINEAR PROGRAMMING;

BENCH-MARK PROBLEMS; BRANCH-AND-BOUND ALGORITHMS; CONTRIBUTION ANALYSIS; FAULT DIAGNOSIS METHOD; MULTIVARIATE STATISTICAL PROCESS MONITORING; RECONSTRUCTION-BASED MULTIVARIATE CONTRIBUTIONS; ROOT CAUSE ANALYSIS; SIGNED DIRECTED GRAPH;

GRAPHIC METHODS;

EID: 84896320460     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2014.02.014     Document Type: Article

References (48)

1. Alcala C.F., Qin S.J. Reconstruction-based contribution for process monitoring. Automatica 2009, 45:1593-1600.
2. AlGhazzawi A., Lennox B. Monitoring a complex refining process using multivariate statistics. Control Engineering Practice 2008, 16:294-307.
3. Chen T., Sun Y. Probabilistic contribution analysis for statistical process monitoring: A missing variable approach. Control Engineering Practice 2009, 17:469-477.
4. Chiang L.H., Braatz R.D. Process monitoring using cause map and multivariate statistics: Fault detection and identification. Chemometrics and Intelligent Laboratory Systems 2003, 65:159-178.
5. Chiang L.H., Kotanchek M.E., Kordon A.K. Fault diagnosis based on fisher discriminant analysis and support vector machines. Computers and Chemical Engineering 2004, 28:1389-1401.
6. Choi S.W., Lee C., Lee J., Park J.H., Lee I. Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems 2005, 75:55-67.
7. Downs J.J., Vogel E.F. A plant-wide industrial process control problem. Computers and Chemical Engineering 1993, 17:245-255.
8. Dubois D., Prade H. Fuzzy sets in approximate reasoning, Part 1: Inference with possibility distributions. Fuzzy Sets and Systems 1991, 40:143-202.
9. Dunia R., Qin S.J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal 1998, 44:1813-1831.
10. Frank P.M. Fault diagnosis in dynamic system using analytical and knowledge-based redundancy - A survey and some new results. Automatic 1990, 26:459-474.
11. Ge Z., Song Z. A distribution-free method for process monitoring. Expert Systems with Applications 2011, 38:9821-9829.
12. Han C., Shih R., Lee L. Quantifying signed directed graphs with the fuzzy set for fault diagnosis resolution improvement. Industrial and Engineering Chemistry Research 1994, 33:1943-1954.
13. He B., Yang X., Chen T., Zhang J. Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach. Journal of Process Control 2012, 22:1228-1236.
14. He B., Zhang J., Chen T., Yang X. Penalized reconstruction-based multivariate contribution analysis for fault isolation. Industrial & Engineering Chemistry Research 2013, 52:7784-7794.
15. He Q.P., Qin S.J., Wang J. A new fault diagnosis method using fault directions in Fisher discriminant analysis. AIChE Journal 2005, 51:555-571.
16. Iri M., Aoki K., O'Shima E., Matsuyama H. An algorithm for diagnosis of system failures in the chemical process. Computers and Chemical Engineering 1979, 3:489-493.
17. Kano M., Nakagawa Y. Data-based process monitoring process control and quality improvement: Recent developments and applications in steel industry. Computers and Chemical Engineering 2008, 32:12-24.
18. Kano M., Tanaka S., Hasebe S., Hashimoto I. Monitoring independent components for fault detection. AIChE Journal 2003, 49:969-976.
19. Kariwala V., Odiowei P.E., Cao Y., Chen T. A branch and bound method for isolation of faulty variables through missing variable analysis. Journal of Process Control 2010, 20:1198-1206.
20. Kim D., Lee I.-B. Process monitoring based on probabilistic PCA. Chemometrics and Intelligent Laboratory Systems 2003, 67:109-123.
21. Ku W., Storer R.H., Georgakis C. Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 1995, 30:179-196.
22. Lee C. Fuzzy logic in control systems: fuzzy logic controller - Part I. IEEE Transactions on Systems, Man and Cybernetics 1990, 20:404-418.
23. Lee G., Han C., Yoon E. Multiple-fault diagnosis of the Tennessee Eastman process based on system decomposition and dynamic PLS. Industrial and Engineering Chemistry Research 2004, 43:8037-8048.
24. Leung D., Romagnoli J. An integration mechanism for multivariate knowledge-based fault diagnosis. Journal of Process Control 2002, 12:15-26.
25. Liu J. Fault diagnosis using contribution plots without smearing effect on non-faulty variables. Journal of Process Control 2012, 22:1609-1623.
26. Lü N., Xiong Z., Wang X., Ren C. Integrated framework of probabilistic signed digraph based fault diagnosis approach to a gas fractionation unit. Industrial and Engineering Chemistry Research 2011, 50:10062-10073.
27. Lyman P.R., Georgakis C. Plant-wide control of the Tennessee Eastman problem. Computers and Chemical Engineering 1995, 19:321-331.
28. MacGregor J., Cinar A. Monitoring, fault diagnosis fault-tolerant control and optimization: Data driven method. Computers and Chemical Engineering 2012, 47:111-120.
29. Martin E.B., Morris A.J., Zhang J. Process performance monitoring using multivariate statistical process control. IEE Proceedings: Control Theory and Applications 1996, 143:134-144.
30. Maurya M.R., Rengaswamy R., Venkatasubramanian V. A systematic framework for the development and analysis of signed digraphs for chemical processes. 1. Algorithms and analysis. Industrial and Engineering Chemistry Research 2003, 42:4789-4810.
31. Miller P., Swanson R.E., Heckler C.F. Contribution plots: A missing link in multivariate quality control. International Journal of Applied Mathematics and Computer Science 1998, 8:775-792.
32. Musulin E., Yelamos I., Puigjaner L. Integration of principal component analysis and fuzzy logic system for comprehensive process fault detection and diagnosis. Industrial and Engineering Chemistry Research 2006, 50:1739-1750.
33. Norvilas A., Negiz A., DeCicco J., Cinar A. Intelligent process monitoring by interfacing knowledge knowledge-based systems and multivariate statistical monitoring. Journal of Process Control 2000, 10:341-350.
34. Qin S.J. Statistical process monitoring: basics and beyond. Journal of Chemometrics 2003, 17:480-502.
35. Qin S.J. Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control 2012, 36:220-234.
36. Raich A., Cinar A. Diagnosis of process disturbances by statistical distance and angle measure. Computers and Chemical Engineering 1997, 6:661-673.
37. Russell E.L., Chiang L.H., Braatz R.D. Data-driven techniques for fault detection and diagnosis in chemical processes 2000, Springer-Verlag, London.
38. Thambirajah J., Benabbas L., Bauer M., Thornhill N.F. Cause-and-effect analysis in chemical processes utilizing XML plant connectivity and quantitative process history. Computers and Chemical Engineering 2009, 33:503-512.
39. Tibshirani R. Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society B 1996, 58:267-288.
40. Westerhuis J.A., Gurden S.P., Smilde A.K. Generalized contribution plots in multivariate statistical process monitoring. Chemometrics and Intelligent Laboratory Systems 2000, 51:95-114.
41. Vedam H., Venkatasubramanian V. PCA-SDG based process monitoring and fault diagnosis. Control Engineering Practice 1999, 7:903-917.
42. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N. A review of process fault detection and diagnosis part I: Quantitative model-based methods. Computers and Chemical Engineering 2003, 27:293-311.
43. Venkatasubramanian V., Rengaswamy R., Kavuri S.N., Yin K. A review of process fault detection and diagnosis part III: Process history based method. Computers and Chemical Engineering 2003, 27:327-346.
44. Yao Y., Gao F. Batch process monitoring in score space of two-dimensional dynamic principal component analysis (PCA). Industrial and Engineering Chemistry Research 2007, 46:8033-8043.
45. Yao Y., Gao F. A survey on multistage/multiphase statistical modeling methods for batch processes. Annual Reviews in Control 2009, 33:172-183.
46. Yi G., Shi J.Q., Choi T. Penalized Gaussian process regression and classification for high-dimensional nonlinear data. Biometrics 2011, 67:1285-1294.
47. Yoon S., MacGregor J.F. Fault diagnosis with multivariate statistical models part I: Using steady state fault signatures. Journal of Process Control 2001, 11:387-400.
48. Zadeh L.A. The role of fuzzy-logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems 1983, 11:199-227.