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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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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