Optimising a fuzzy fault classification tree by a single-objective genetic algorithm

• Autorzy: Zio E. , Baraldi P. , Popescu I. C.
• Języki publikacji: EN

Abstrakty

EN

In this paper a single-objective Genetic Algorithm is exploited to optimise a Fuzzy Decision Tree for fault classification. The optimisation procedure is presented with respect to an ancillary classification problem built with artificial data. Work is in progress for the application of the proposed approach to a real fault classification problem.

Słowa kluczowe

EN

fault classification; decision tree; fuzzy logic; genetic algorithm

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2007
• Tom: Vol. 2
• Strony: 391--400
• Opis fizyczny: Bibliogr. 26 poz., rys., tab.

Twórcy

autor

Zio E.

• Department of Nuclear Engineering, Polytechnic of Milan, Milan, Italy

autor

Baraldi P.

• Department of Nuclear Engineering, Polytechnic of Milan, Milan, Italy

autor

Popescu I. C.

• Department of Nuclear Engineering, Polytechnic of Milan, Milan, Italy

Bibliografia

• [1] Chambers, L. (1995). Practical handbook of genetic algorithms: applications. Vol. I; New frontiers. Vol. II. CRC Press, 1995.
• [2] Coello, C. A. (2000). Treating Constraints as Objectives for Single-Objective Evolutionary Optimisation. Engineering Optimisation. Vol. 32, 275-308.
• [3] De Jong, K. (1990). Genetic-algorithm-based learning, Machine learning: an artificial intelligence approach. Morgan Kaufmann Publishers Inc., San Francisco.
• [4] Deb, K. (1999). Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation Journal. Vol. 7, 205-230.
• [5] Du, J. & Er, M. J. (2004). Fault Diagnosis in Air-Handling Unit System Using Dynamic Fuzzy Neural Network. Proceedings of the sixth International FLINS Conference, 483-488.
• [6] Eiben, D. A. E. & Smith, J. E. (2003). Introduction to Evolutionary Computing. Springer.
• [7] Goddu, G., Li, B. Chow, M. Y. & Hung, J. C. (1998). Motor Bearing Fault Diagnosis by a Fundamental Frequency Amplitude Based Fuzzy Decision System. IEEE Trans. on Systems, Man, Cybernetics, 1961-1965.
• [8] Goldberg, D. E. (1989). Genetic algorithms in search, optimisation, and machine learning”, Addison-Wesley Publ. Co.
• [9] Haupt, R. L. & Haupt, S. H. (1998). Practical genetic algorithms. J. Wiley & Sons, New York.
• [10] Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with application to biology. Control and Artificial Intelligence. MIT Press, 4-th edition.
• [11] Isermann, R. & Ballé, P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice. No. 5, 709-719.
• [12] Keller, J.M., Gray, M. R. & Givens J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst., Man, Cybern. SMC-15 (4), 580-585.
• [13] Kim, K. O. & Zuo, M. J. Two Fault Classification Methods for Large Systems when Available Data is Limited.
• [14] Lee, C., Alena, R. L. & Robinson, P. (2005). Migrating Fault Trees To Decision Trees For Real Time Fault Detection On International Space Station. Aerospace, 2005 IEEE Conference, 1-6.
• [15] Leonhardt, S. & Ayoubi, M. (1997). Methods of fault diagnosis. Control Engineering Practice. No. 5, 683-692.
• [16] Marseguerra, M., Zio, E. & Podofillini, L. (2003). Model parameters estimation and sensitivity by genetic algorithms. Annals of Nuclear Energy 30, 1437-1456.
• [17] Michaelwicz, Z. (1999). Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York.
• [18] Mitchell, M. (1996). An introduction to genetic algorithms, MIT Press, London.
• [19] Puska, E. & Noemann, S. (2002). 3-d core studies for Hambo simulator. Proceedings of Presentation on Man-Machine System Research, Enlarged Halden Programme Group Meeting. Vol. 2, September.
• [20] Rovnyak, S., Kretsinger, S., Thorp, J. & Brown, D. (1994). Decision trees for real-time transient stability prediction. Power Systems. IEEE Transactions on Systems, Vol. 9, No.3, 1417-1426.
• [21] Roverso, D. (2003). Fault diagnosis with the Aladdin transient classifier. Proceedings of System Diagnosis and Prognosis: Security and Condition Monitoring Issues III, AeroSense2003, Aerospace and Defence Sensing and Control Technologies Symposium, Orlando, FL, USA.
• [22] Uhrig, R. E. & Tsoukalas, L. H. (1999). Soft computing technologies in nuclear engineering applications. Progress in Nuclear Energy, Elsevier Science, 34, 13-75.
• [23] Zio, E., Baraldi, P. & Roverso, D. (2005). An extended classificability index for feature selection in nuclear transients. Annals of Nuclear Energy. No. 32, 1632-1649.
• [24] Zio, E., Baraldi, P. & Pedroni, N. (2006). Selecting Features for Nuclear Transients Classification by means of Genetic Algorithms. IEEE Transaction on Nuclear Science. Vol. 53, No. 3, 1479-1493.
• [25] Zio, E., Baraldi, P. & Popescu, I. C. From fuzzy clustering to a rule-based model for fault classification.
• [26] Zio, E. Baraldi, P. & Popescu, I. C. A Fuzzy Decision Tree for Fault Classification.