Fault detection and classification using Kalman filter and genetic neuro-fuzzy systems

Haris M. Khalid, Amar Khoukhi, Fouad M. Al-Sunni
2011 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 possible
more » ... with not only confirmation of the findings but also an accurate unfolding-in-time of the finer details of the fault, thus completing the overall fault diagnosis picture of the system under test. The proposed scheme is evaluated extensively on a two-tank process used in industry exemplified by a benchmarked laboratory scale coupled-tank system.
doi:10.1109/nafips.2011.5751925 fatcat:4gzp72oqc5bmxku46dxmg4b5mu