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Fault Detection and Classification using Kalman Filter and Hybrid Neuro-Fuzzy Systems

by A. Khoukhi, H. Khalid, R. Doraiswami, L. Cheded
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 22
Year of Publication: 2012
Authors: A. Khoukhi, H. Khalid, R. Doraiswami, L. Cheded

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 },
issue_date = { May 2012 },
volume = { 45 },
number = { 22 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { },
doi = { 10.5120/7079-9312 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:38:14.305140+05:30
%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
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 22
%P 7-14
%D 2012
%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.

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Index Terms

Computer Science
Information Sciences


Kalman Filter Soft Computing Ann Genetic Algorithm Anfis Fault Detection Fault Isolation Benchmarked Laboratory Scale Two-tank Systems