International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 45 - Issue 22 |
Published: May 2012 |
Authors: A. Khoukhi, H. Khalid, R. Doraiswami, L. Cheded |
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A. Khoukhi, H. Khalid, R. Doraiswami, L. Cheded . Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems. International Journal of Computer Applications. 45, 22 (May 2012), 7-14. DOI=10.5120/7079-9312
@article{ 10.5120/7079-9312, author = { A. Khoukhi,H. Khalid,R. Doraiswami,L. Cheded }, title = { Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 45 }, number = { 22 }, pages = { 7-14 }, doi = { 10.5120/7079-9312 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A A. Khoukhi %A H. Khalid %A R. Doraiswami %A L. Cheded %T Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems%T %J International Journal of Computer Applications %V 45 %N 22 %P 7-14 %R 10.5120/7079-9312 %I Foundation of Computer Science (FCS), NY, USA
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 time, 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.