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2011 Annual Meeting of the North American Fuzzy Information Processing Society
In this paper, an efficient scheme to detect and classify faults in a system using kalman filtering and hybrid neuro-fuzzy computing techniques, respectively, is proposed. A fault is detected whenever the moving average of the Kalman filter residual exceeds a threshold value. The fault classification has been made effective by implementing a hybrid neuro-fuzzy Inference system. By doing so, the critical information about the presence or absence of a fault is gained in the shortest possibledoi:10.1109/nafips.2011.5751925 fatcat:4gzp72oqc5bmxku46dxmg4b5mu