Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets

Engineering Applications of Artificial Intelligence

Volumn 17, Issue 5, 2004, Pages 501-518

  Maurya, Mano Ram ^a,c   Rengaswamy, Raghunathan ^b   Venkatasubramanian, Venkat ^a  

^a PURDUE UNIVERSITY   (United States)

^b Wallace H Coulter School of Engineering   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Control loops; Flowsheet analysis; Incipient fault diagnosis; Initial response; Qualitative analysis; Signed digraphs (SDG); Steady state response

Indexed keywords

CHEMICAL ANALYSIS; COMPUTER SIMULATION; CONTROL EQUIPMENT; FLOWCHARTING; GRAPH THEORY; MATHEMATICAL MODELS;

CONTROL LOOPS; FLOWSHEET ANALYSIS; INCIPIENT FAULT DIAGNOSIS; INITIAL RESPONSES; QUALITATIVE ANALYSIS; SIGNED DIGRAPHS (SDG); STEADY-STATE RESPONSE;

ARTIFICIAL INTELLIGENCE;

EID: 3142733714     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2004.03.007     Document Type: Article

References (48)

1. Bhushan M., Rengaswamy R. Design of sensor location based on various fault diagnostic observability and reliability criteria. Computers and Chemical Engineering. 24:2000;735-741.
2. Bhushan M., Rengaswamy R. Design of sensor network based on the signed directed graph of the process for efficient fault diagnosis. Industrial and Engineering Chemistry Research. 39:2000;999-1019.
3. Bhushan M., Rengaswamy R. Comprehensive design of a sensor network for chemical plants based on various diagnosability and reliability criteria. 1. Framework Industrial and Engineering Chemistry Research. 41(7):2002;1826-1839.
4. Bhushan M., Rengaswamy R. Comprehensive design of a sensor network for chemical plants based on various diagnosability and reliability criteria. 2. Applications Industrial and Engineering Chemistry Research. 41(7):2002;1840- 1860.
5. Braatz, R., Chiang, L., 2001. Process monitoring using causal map and multivariate statistics. In: AIChE Annual Meeting, 282e, Reno, NV, November 4-9.
6. Chang C., Yu C. On-line fault diagnosis using the signed directed graph. Industrial Engineering and Chemical Research. 29(7):1990;1290-1299.
7. Chang S., Chang C. A fuzzy-logic based fault diagnosis strategy for process control loops. Chemical Engineering Science. 58(15):2003;3395-3411.
8. Chen J., Howell J. A self-validating control system based approach to plant fault detection and diagnosis. Computers and Chemical Engineering. 25:2001;337-358.
9. Dash S., Maurya M.R., Rengaswamy R., Venkatasubramanian V. A novel interval-halving framework for automated identification of process trends. AIChE Journal. 50(1):2004;149-162.
10. Donoho D.L., Johnstone I.M. Ideal spatial adaptation via wavelet shrinkage. Biometrika. 81(3):1994;425-455.
11. Downs J.J., Vogel E.F. A plant-wide industrial process control problem. Computers and Chemical Engineering. 17(3):1993;245.
12. Gal I., Varga J., Hangos K. Integrated structure design of a process and its control system. Journal of Process Control. 8(4):1998;251-263.
13. Hangos K., Tuza Z. Optimal control structure selection for process systems. Computers and Chemical Engineering. 25(11-12):2001;1521-1536.
14. Iri M., Aoki K., O'Shima E., Matsuyama H. An algorithm for diagnosis of system failures in the chemical process. Computers and Chemical Engineering. 3(1-4):1979;489-493.
15. Kramer M., Palowitch B. Rule-based approach to fault diagnosis using the signed directed graph. AIChE Journal. 33(7):1987;1067-1078.
16. Kumar V. Algorithms for constraints satisfaction problems. a survey The AI Magazine. 13(1):1992;32-44.
17. Kuo D., Hsu D., Chang C., Chen D. Prototype for integrated hazard analysis. AICHE Journal. 43(6):1997;1494-1510.
18. Lee G., Song S., Yoon E. Multiple-fault diagnosis based on system decomposition and dynamic PLS. Industrial and Engineering Chemistry Research. 42(24):2003;6145-6154.
19. Li R., Wang X. Qualitative/quantitative simulation of process temporal behavior using clustered fuzzy digraphs. AIChE Journal. 47(4):2001;906-919.
20. Liu, Z., 1998. Algorithms for constraint satisfaction problems. Master's Thesis, University of Waterloo.
21. Maurya, M.R., 2003. Integrating Causal Models and Trend Analysis for Process Fault Diagnosis. Ph.D. Thesis, Purdue University (2003).
22. Maurya, M.R., Rengaswamy, R., Venkatasubramanian, V., 2001. Systematic development and application of digraphs for process diagnosis and hazards analysis. In: Stephanopoulos, G., Romagnoli, J.A., Yoon, E.S., (Eds.), 4th IFAC Workshop on On-Line Fault Detection and Supervision in the Chemical Process Industries (CHEMFAS-4), Korea, June 7-8, Elsevier Science Ltd., Amsterdam, pp. 327-332.
23. Maurya, M.R., Rengaswamy, R., Venkatasubramanian, V., 2002. A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis. Technical Report CIPAC-02-1, Purdue University (2002).
24. Maurya, M.R., Rengaswamy, R., Venkatasubramanian, V., 2003a. Consistent malfunction diagnosis inside control loops using signed directed graphs. In: Kraslawski, A., Turune, I., (Eds.), Proceedings of the 13th European Symposium on Computer Aided Process Engineering (ESCAPE-13), June 1-4, 2003, Lappeenranta, Finland, Elsevier Science Ltd., Amsterdam, pp. 473-478.
25. 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. 42(20):2003;4789-4810.
26. Maurya M.R., Rengaswamy R., Venkatasubramanian V. A systematic framework for the development and analysis of signed digraphs for chemical processes. 2. Control loops and flowsheet analysis. Industrial and Engineering Chemistry Research. 42(20):2003;4811-4827.
27. Mylaraswamy, D., Kavuri, S., Venkatasubramanian, V., 1994. A framework for automated development of causal models for fault diagnosis. AIChE Annual Meeting, 232g, Miami (1994).
28. Nam D., Han C., Jeong C., Yoon E. Automatic construction of extended symptom-fault associations from the signed digraph. Computers and Chemical Engineering. 20:1996;S605-S610.
29. Oyeleye O., Kramer M. Qualitative simulation of chemical process systems. steady-state analysis AIChE Journal. 34(9):1988;1441-1454.
30. Palmer C., Chung P. Eliminating ambiguities in qualitative causal feedback. Computers and Chemical Engineering. 22:1998;S843-S846.
31. Palmer C., Chung P. Creating signed directed graph models for process plants. Industrial and Engineering Chemistry Research. 39:2000;2548-2558.
32. Raghuraj R., Bhushan M., Rengaswamy R. Location of sensors in complex chemical plants based on fault diagnostic observability criteria. AIChE Journal. 45:1999;310-322.
33. Ricker N.L., Lee J.H. Nonlinear model predictive control of the Tennessee Eastman challenge process. Computers and Chemical Engineering. 19(9):1995;961-982.
34. Shih R., Lee L. Use of fuzzy cause-effect digraph for resolution fault diagnosis for process plants I. Fuzzy cause-effect digraph. Industrial Engineering and Chemical Research. 34(5):1995;1688-1702.
35. Shiozaki J., Matsuyama H., O'Shima E., Iri M. An improved algorithm for diagnosis of system failures in the chemical process. Computers and Chemical Engineering. 9(3):1985;285-293.
36. Takeda K., Shibata B., Tsuge Y., Matsuyama H. Improvement of fault diagnostic system utilizing signed directed graph - the method using transfer delay of failure. Transactions of the Society of Instrument and Control Engineers. 31(1):1995;98-107.
37. Tarifa E., Scenna N. Fault diagnosis, directed graphs, and fuzzy logic. Computers and Chemical Engineering. 21:1997;S649-S654.
38. Tsuge, Y., Shiozaki, J., Matsuyama, H., O'Shima, E., 1985. Fault diagnosis algorithms based on signed directed graphs and its modifications. Industrial Chemical Engineering Symposium Series 92, 133.
39. Umeda T., Kuriyama T., Ichikawa A. A logical structure for process control system synthesis. Niemi A. A link between Science and Applications of Automatic Control. 1979;Pergamon Press Ltd. Oxford.
40. Umeda T., Kuriyama T., O'Shima E., Matsuyama H. A graphical approach to cause and effect analysis of chemical processing systems. Chemical Engineering Science. 35(12):1980;2379-2388.
41. Vaidhyanathan R., Venkatasubramanian V. Digraph-based models for automated HAZOP analysis. Reliable Engineering Systems Safety. 50(1):1995;33-49.
42. Venkatasubramanian V., Rengaswamy R., Kavuri S. A review of process fault detection and diagnosis Part II. qualitative models and search strategies Computers and Chemical Engineering. 27(3):2003;313-326.
43. Vianna R., McGreavy C. Qualitative modeling of chemical processes - a weighted digraph (WDG) approach. Computers and Chemical Engineering. 19:1995;S375-S380.
44. Viswanathan S., Shah N., Venkatasubramanian V. A hybrid strategy for batch process hazards analysis. Computers and Chemical Engineering. 24(2-7):2000;545-549.
45. Wang X., Chen B., Yang S., McGreavy C. Neural nets, fuzzy sets and digraphs in safety and operability studies of refinery reaction processes. Computers and Chemical Engineering. 21(10):1996;2169-2178.
46. Wang Y., Chang C. A hierarchical approach to construct petri nets for modeling the fault propagation mechanisms in, sequential operations. Computers and Chemical and Engineering. 27(2):2003;259-280.
47. Wilcox N., Himmelblau D. The possible cause-effect graphs (PSEG) model for fault diagnosis - I. Methodology. Computers and Chemical Engineering. 18(2):1994;103-116.
48. Wilcox N., Himmelblau D. The possible cause-effect graphs (PSEG) model for fault diagnosis - II. Applications. Computers and Chemical Engineering. 18(2):1994;117-127.