Technical diagnostic of a fleet of vehicles using rough set theory

European Journal of Operational Research

Volumn 193, Issue 3, 2009, Pages 891-903

  Sawicki, Piotr ^a   Zak, Jacek ^a  

^a POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Data mining; Rough sets; Transportation

Indexed keywords

FLEET OPERATIONS; FUZZY SETS; RECONNAISSANCE AIRCRAFT; SET THEORY; VEHICLES;

COMPUTATIONAL EXPERIMENTS; CROSS VALIDATIONS; DATA MINING; DECISION RULES; DELIVERY SYSTEMS; DIAGNOSTIC PROCESSING; ROUGH SETS; TECHNICAL CONDITIONS; TRANSPORTATION;

ROUGH SET THEORY;

EID: 53449083350     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2007.10.053     Document Type: Article

References (42)

1. Azibi R., and Vanderpooten D. Construction of rule-based assignment models. European Journal of Operational Research 138 3 (2002) 274-293
2. Bazan, J., Skowron, A., Synak, P., 1994. Dynamic reducts as a tool for extracting laws from decision tables. In: Proceedings of the 8th International Symposium ISMIS '94: Methodologies for Intelligent Systems. Lecture Notes in Artificial Intelligence, vol. 869, pp. 346-355.
3. Bazan J., Skowron A., and Synak P. Discovery of decision rules from experimental data. In: Lin T.Y., and Wildberger A.M. (Eds). Soft Computing (1995), Society for Computer Simulation, San Diego 276-279
4. Brown E.M. Boolean Reasoning (1990), Kluwer Academic Publishers, Dordrecht
5. Campbell J.D., and Jardine A.K.S. Maintenance Excellence - Optimizing Equipment Life-cycle Decisions (2001), Marcel Dekker, New York
6. Caplen R. A Practical Approach to Reliability (1972), Business Books Limited, London
7. Dimitras A.I., Słowiński R., Susmaga R., and Zopounidis C. Business failure prediction using rough sets. European Journal of Operational Research 114 2 (1999) 263-280
8. Greco S., Matarazzo B., and Słowiński R. A new rough set approach to multicriteria and multiattribute classification. In: Polkowski L., and Skowron A. (Eds). Rough Set and Current Trends in Computing, Conference Proceedings RSTCTC'98 (1998), Springer 60-67
9. Greco S., Matarazzo B., and Słowiński R. A new rough set approach to multicriteria and multiattribute classification. Lecture Notes in Artificial Intelligence 1424 (1998) 60-67
10. Greco S., Matarazzo B., and Słowiński R. The use of rough sets and fuzzy sets in MCDM. In: Gel T., Stewart T.J., and Hanne T. (Eds). Multicriteria Decision Making. Advances in MCDM Models, Algorithms, Theory, and Applications (1999), Kluwer Academic Publishers, Dordrecht 14.1-14.59
11. Greco S., Matarazzo B., and Słowiński R. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129 1 (2001) 1-47
12. Greco S., Matarazzo B., and Słowiński R. Multicriteria classification by dominance-based rough set approach. Methodological basis of the 4eMka system. In: Kloesgen W., and Zytkow J. (Eds). Handbook of Data Mining and Knowledge Discovery (2002), Oxford University Press, New York 318-328
13. Greco S., Pawlak Z., and Słowiński R. Can Bayesian confirmation measures be useful for rough set decision rules?. Engineering Application of Artificial Intelligence 17 (2004) 345-361
14. Hilderman R., and Hamilton H. Knowledge Discovery and Measures of Interest (2001), Kluwer Academic Publishers
15. Ilyakhinskii A.V. Statistical approach to diagnostics of structures on the basis of acoustic emission signals. NDT & E International 25 6 (1992) 294-297
16. Isermann R. Process fault detection based on modelling and estimation methods - a survey. Automatica 20 4 (1984) 387-404
17. Kelley, A., Harris, M., 1987. Management of industrial maintenance. London.
18. Larischev O.I., and Moshkovich H.M. An approach to ordinal classification problems. International Transactions on Operation Research 1 3 (1994) 375-385
19. Li J.R., Khoo L.P., and Tor S.B. RMINE: A rough set based data mining prototype for the reasoning of incomplete data in condition-based fault diagnosis. Journal of Intelligent Manufacturing 17 (2006) 163-176
20. Lu Y., Chen T.Q., and Hamilton B. A fuzzy diagnostic model and its application in automotive engineering diagnosis. Applied Intelligence 9 (1998) 231-243
21. McGarry K. A survey of interestingness measure for knowledge discovery. The Knowledge Engineering Review 20 1 (2005) 39-61
22. Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data (1991), Kluwer, Dordrecht
23. ROSE2-Rough set data explorer, 2005. Laboratory of Intelligent Decision Support Systems, Poznań University of Technology, Poznań. .
24. Sawicki, P., 2003. Quality evaluation method of transportation system using rough sets theory. Doctoral dissertation. Warsaw University of Technology, Warsaw (in Polish).
25. Segal L. Diagnostic method for vehicle breaks. NDT& E International 32 7 (1999) 369-373
26. Sincebaugh P., and Green W. A neural network diagnostic test system for armored vehicle shock absorber. Expert Systems With Applications 11 2 (1996) 237-244
27. Skowron, A., 1993. Boolean reasoning for decision generation. In: Komorowski, J., Ras, Z. (Eds.), Proceedings of the 7th International Symposium ISMIS '93, Trondheim, Norway, 1993. Lecture Notes in Artificial Intelligence, vol. 689, pp. 295-305.
28. Skowron A. Extracting laws from decision tables: A rough set approach. Computational Intelligence 11 2 (1995) 371-388
29. Skowron A., and Polkowski L. Decision algorithms: A survey of rough set - theoretic methods. Fundamenta Informaticae 30 3/4 (1997) 345-358
30. Słowiński, R., 1993. Rough set learning of preferential attitude in multi-criteria decision making. In: Komorowski, J., Ras, Z., (Eds.), Methodologies for Intelligent Systems. Lecture Notes in Artificial Intelligence, vol. 689, pp. 642-651.
31. Słowiński, R., 1999. Rough set data analysis - a new way of solving some decision problems in transportation. In: Adamski, A., Rudnicki, R., Żak, J., (Eds.), Modeling and Management in Transportation. Conference Proceedings, Poznań-Kraków, vol. 1, pp. 63-66.
32. Słowiński R., and Stefanowski J. Rough classification with valued closeness relation. In: Diday E., Lechevallier Y., Schrader M., Bertrandt P., and Burtschy B. (Eds). New Approach in Classification and Data Analysis (1994), Springer-Verlag, Berlin 482-489
33. Słowiński R., and Zopounidis C. Application of the rough set approach to evaluation of bankruptcy risk. International Journal of Intelligent Systems in Accounting, Finance and Management 4 1 (1995) 1-12
34. Stefanowski J. On rough set based approaches to induction of decision rules. In: Skowron A., and Polkowski L. (Eds). Rough Sets in Data Mining and Knowledge Discovery vol. 1 (1998), Physica-Verlag, Heidelberg 530-553
35. Stoustrup J., and Niemann H.H. Fault estimation - a standard problem approach. International Journal of Robust and Nonlinear Control 12 8 (2002) 649-673
36. System 4eMka, 2005. A rule system for multicriteria decision support integrating dominance relation with rough approximation. Laboratory of Intelligent Decision Support Systems, Poznań University of Technology, Poznań, .
37. Thomas J.-H., Dubuisson B., and Dillies-Peltier M.-A. Engine knock detection from vibration signal using pattern recognition. Meccanica 32 (1997) 431-439
38. Wang Q.H., and Li J.R. A rough set-based fault ranking prototype system for fault diagnosis. Engineering Applications of Artificial Intelligence 17 (2004) 909-917
39. Weiss S.M., and Kulikowski C.A. Computer systems that learning: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems (1990), Morgan Kaufmann, San Mateo
40. Żak, J., 1999. The application of rough sets theory in the fleet management of transportation companies. In: Proceedings of the 11th Mini Euro Conference: Artificial Intelligence in Transportation Systems and Science. Helsinki, article LIII.
41. Żak, J., Stefanowski, J., 1994. Determining maintenance of motor vehicles using rough sets approach. In: Conference Proceedings, Euromaintenance'94, Amsterdam, pp. 39-42.
42. Żak, J., Sawicki, P., Redmer, A., 1999. Application of rough sets theory to the management of tram cars. In: Conference Proceedings: Traffic Management in the Cities under Congestion. Poznań, vol. 1, pp. 280-288.