Fault detection and isolation based on fuzzy automata
Study of the use of fuzzy automata for fault detection and isolation
Year of Publication2009
Fuzzy automata are proposed for fault diagnosis. The output of the monitored system is partitioned into linear segments which in turn are assigned to pattern classes (templates) with the use of 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, and 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.Citation
Rigatos, G. G. (2009) "Fault detection and isolation based on fuzzy automata"Information Sciences 179 (12) pp 1893–1902
This item is categorised as follows
Additional keywords/tagssyntactic analysispattern matchingfault detection and isolationfuzzy automata
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