Fault diagnosis using dynamic trend analysis: A review and recent developments

Engineering Applications of Artificial Intelligence

Volumn 20, Issue 2, 2007, Pages 133-146

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

^a PURDUE UNIVERSITY   (United States)

^b Clarkson University   (United States)

Author keywords

Fault diagnosis; Intelligent control; Interval halving; Qualitative simulation; Qualitative trend analysis; Similarity search; Trend matching

Indexed keywords

FEATURE EXTRACTION; FUZZY SETS; HIERARCHICAL SYSTEMS; PARAMETER ESTIMATION; SIGNAL PROCESSING;

FAULT DIAGNOSIS; INTERVAL-HALVING; QUALITATIVE SIMULATION; QUALITATIVE TREND ANALYSIS; SIMILARITY SEARCH; TREND-MATCHING;

FAULT TOLERANT COMPUTER SYSTEMS;

EID: 33751399694     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2006.06.020     Document Type: Article

References (39)

1. Agrawal, R., Lin, K., Sawhney, H.S., Shim, K., 1995. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. Proceedigs of the 21st international conference on very large databases, Zurich, Switzerland.
2. Bakshi B.R., and Stephanopoulos G. Representation of process trends-III. Multi-scale extraction of trends from process data. Computers and Chemical Engineering 18 4 (1994) 267-302
3. Bakshi B.R., and Stephanopoulos G. Representation of process trends-IV. Induction of real-time patterns from operating data for diagnosis and supervisory control. Computers and Chemical Engineering 18 4 (1994) 303-332
4. Bhushan M., and 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. Cheung J.T., and Stephanopoulos G. Representation of process trends-Part I. A formal representation framework. Computers and Chemical Engineering 14 4/5 (1990) 495-510
6. Colomer, J., Meléndez, J., Gamero, F.I., 2002. Pattern recognition based on episodes and DTW. Application to diagnosis of a level control system. In: Proceedings of the 16th International Workshop on Qualitative Reasoning QR2002. Barcelona, Catalonia, Spain, June 10-12. URL 〈http://www.upc.es/web/QR2002/Papers/QR2002%20-%20Colomer.pdf〉
7. Dash S., Rengaswamy R., and Venkatasubramanian V. Fuzzy-logic based trend classification for fault diagnosis of chemical processes. Computers and Chemical Engineering 27 3 (2003) 347-362
8. Dash S., Maurya M.R., Rengaswamy R., and Venkatasubramanian V. A novel interval-halving framework for automated identification of process trends. AIChE Journal 50 1 (2004) 149-162
9. Downs J.J., and Vogel E.F. A plant-wide industrial process control problem. Computers and Chemical Engineering 17 3 (1993) 245-255
10. Faloutsos, C., Ranganathan, M., Manolopoulos, Y., 1994. Fast subsequence matching in time-series databases. In: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, Minneapolis, MN, May 24-27.
11. Forbus K. Qualitative process theory. Artificial Intelligence 24 (1984) 85-168
12. Hetland, M.L., 2001. A survey of recent methods for efficient retrieval of similar time sequences. First NTNU Computer Science Graduate Student Conference 2001, Dragvoll, Trondheim, Tuesday May 8th. URL 〈citeseer.nj.nec.com/hetland01survey.html〉
13. Janusz M., and Venkatasubramanian V. Automatic generation of qualitative description of process trends for fault detection and diagnosis. Engineering Applications of Artificial Intelligence 4 5 (1991) 329-339
14. Kassidas A., MacGregor J.F., and Taylor P.A. Synchronization of batch trajectories using dynamic time warping. AIChE Journal 44 4 (1998) 864-875
15. Kassidas A., Taylor P.A., and MacGregor J.F. Off line diagnosis of deterministic faults in continuous dynamic multivariate processes using speech recognition methods. Journal of Process Control 8 5-6 (1998) 381-393
16. Keogh, E.J., Chu, S., Hart, D., Pazzani, M.J., 2001. An online algorithm for segmenting time series. In: IEEE International Conference on Data Mining ICDM, November 29-December 2, San Jose, CA. IEEE Computer Society, Los Alamitos, CA, pp. 289-296.
17. Konstantinov K.B., and Yoshida T. Real-time qualitative analysis of the temporal shapes of (Bio)process variables. AIChE Journal 38 11 (1992) 1703-1715
18. Kuipers, B., 1985. The limits of qualitative simulation. In: Proceedings of Ninth Joint International Conference on Artificial Intelligence, (IJCAI-85), pp. 128-136.
19. Lu N., Wang F., and Gao F. Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis. Industrial and Engineering Chemistry Research 42 18 (2003) 4198-4207
20. Mah R.S.H., Tamhane A.C., Tung S.H., and Patel A.N. Process trending with piecewise linear smoothing. Computers and Chemical Engineering 19 2 (1995) 129-137
21. Maurya, M.R., 2003. Integrating causal models and trend analysis for process fault diagnosis. Ph.D. thesis, Purdue University.
22. Maurya, M.R., Rengaswamy, R., Venkatasubramanian, V., 2004. A framework for on-line trend extraction and fault diagnosis. In: Proceedings of the International Symposium on Advanced Control of Chemical Processes (ADChEM-2003), Hong Kong, January 11-14. Hong Kong University of Science and Technology, pp. 423-428.
23. Perng, C., Wang, H., Zhang, S.R., Parker, D.S., 2000. Landmarks: a new model for similarity-based pattern querying in time series databases. In: Sixteenth International Conference on Data Engineering (ICDE 2000), San Diego, CA, February 28-March 03, pp. 33-44.
24. Rengaswamy R., Hagglund T., and Venkatasubramanian V. A qualitative shape analysis formalism for monitoring control loop performance. Engineering Applications of Artificial Intelligence 14 1 (2001) 23-33
25. Rengaswamy R., and Venkatasubramanian V. A syntactic pattern-recognition approach for process monitoring and fault diagnosis. Engineering Applications of Artificial Intelligence 8 1 (1995) 35-51
26. Shatkay, H., 1995. Approximate queries and representations for large data sequences. Tech. Rep. CS-95-03, Department of Computer Science, Brown University, Providence, RI.
27. Srinivasan, R., Qian, M., 2002. Online monitoring of process states using dynamic programming and dynamic time warping. In: AIChE Annual Meeting, 263g, Indianapolis, IN, November 3-8.
28. Srinivasan R., Viswanathan P., Vedam H., and Nochur A. A framework for managing transitions in chemical plants. Computers and Chemical Engineering 29 2 (2005) 305-322
29. Sugiura N., and Ogden R.T. Testing change-points with linear trend. Communications in Statistics B: Simulation and Computation 23 (1994) 287-322
30. Sun W., Palazoglu A., and Romagnoli J.A. Detecting abnormal process trends by wavelet-domain hidden Markov models. AIChE Journal 49 1 (2003) 140-150
31. Sundarraman A., and Srinivasan R. Monitoring transitions in chemical plants using enhanced trend analysis. Computers and Chemical Engineering 27 10 (2003) 1455-1472
32. The ASM consortium, 1992 〈http://www.asmconsortium.com/〉.
33. Vedam, H., 1999. Op-aide: an intelligent operator decision support system for diagnosis and assessment of abnormal situations in process plants. Ph.D. thesis, Purdue University.
34. Vedam, H., Venkatasubramanian, V., 1997. A wavelet theory-based adaptive trend analysis system for process monitoring and diagnosis. In: American Control Conference, pp. 309-313.
35. Vedam H., Venkatasubramanian V., and Bhalodia M. A B-Spline based method for data compression, process monitoring and diagnosis. Computers and Chemical Engineering 22 (1998) S827-S830
36. Vingron M., and Argos P. A fast and multiple sequence alignment algorithm. Computer Applications in the Biosciences 5 (1989) 115-121
37. Williams, B.C., 1986. Doing time: putting qualitative reasoning on firmer ground. In: Proceedings of the Fifth National Conference on Artificial Intelligence AAAI 86, vol. 1, August 1-11, MIT Press, Cambridge, MA, Philadelphia, PA, USA, pp. 105-112.
38. Wong J., McDonald K., and Palazoglu A. Classification of process trends based on fuzzified symbolic representation and hidden Markov models. Journal of Process Control 8 5-6 (1998) 395-408
39. Wong J., McDonald K., and Palazoglu A. Classification of abnormal plant operation using multiple process variable trends. Journal of Process Control 11 4 (2001) 409-418