Abstract
Fuzzy automata are proposed for fault diagnosis. The output of the monitored system is partitioned into linear segments which are assigned to pattern classes (templates) with the use of fuzzy membership functions. A sequence of templates is generated and becomes input to fuzzy automata which have transitions that correspond to the templates of the properly functioning system. If the automata reach their final states, i.e. the input sequence is accepted by the automata with a membership degree that exceeds a certain threshold, then normal operation is deduced, otherwise, a failure is diagnosed. Fault diagnosis of a DC motor and detection of abnormalities in the ECG signal are used as case studies.
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References
Kozen, D.C.: Automata and Computability. Springer, Heidelberg (1997)
Fuzzy Automata and Languages. Chapman & Hall, Boca Raton (2002)
Tzafestas, S.G., Singh, M.G., Schmidt, G.: System fault diagnosis, reliabilty and related Knowledge-based Approaches. Fault Diagnostics and Reliability, vol. 1. Knowledge-based and Fault Tolerant techniques, vol. 2 Reidel, Dordrecht (1989)
Tümer, M., Belfore, L., Ropella, K.: A Syntactic Methodology for Automatic Diagnosis by Analysis of Continuous Time Measurements Using Hierarchical Signal Representations. IEEE Trans. on Systems, Man and Cybernetics - Part B: Cybernetics (2003)
Steimann, F., Adlassnig, K.P.: Clinical monitoring with fuzzy automata. Fuzzy Sets and Systems 61, 37–42 (1994)
Koski, A., Juhola, M., Meriste, M.: Syntactic Recognition of ECG signals by attributed finite automata. Pattern Recognition 28, 1927–1940 (1995)
Martins, J.F., Pires, A.J., Vilela Mendes, R., Dente, J.: Modelling Electromechanical Drive Systems: A Formal Language Approach. In: Proc. of 35th IEEE Industry Applications Society Annual Meeting, IAS 2000, Rome, Italy (October 2000)
Trahanias, P., Skordalakis, E., Papakonstantinou, G.: A syntactic method for the classification of the QRS patterns. Pattern Recognition Letters 9, 13–18 (1989)
Trahanias, P., Skordalakis, E.: Syntactic Pattern Recognition of the ECG. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 648–657 (1990)
Tümer, M.B., Belfore, L.A., Ropella, K.M.: Applying hierarchical fuzzy automata to automatic diagnosis. In: Proc. Mtg. North America Fuzzy Information Process. Syst., Pensacola, FL (1998)
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Rigatos, G.G., Tzafestas, S.G. (2004). Fuzzy Automata for Fault Diagnosis: A Syntactic Analysis Approach. In: Vouros, G.A., Panayiotopoulos, T. (eds) Methods and Applications of Artificial Intelligence. SETN 2004. Lecture Notes in Computer Science(), vol 3025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24674-9_32
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DOI: https://doi.org/10.1007/978-3-540-24674-9_32
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