10.5120/7079-9312 |
A Khoukhi, H Khalid, R Doraiswami and L Cheded. Article: Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems. International Journal of Computer Applications 45(22):7-14, May 2012. Full text available. BibTeX
@article{key:article, author = {A. Khoukhi and H. Khalid and R. Doraiswami and L. Cheded}, title = {Article: 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}, month = {May}, note = {Full text available} }
Abstract
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.
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