Research Article

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

by  A. Khoukhi, H. Khalid, R. Doraiswami, L. Cheded
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Issue 22
Published: May 2012
Authors: A. Khoukhi, H. Khalid, R. Doraiswami, L. Cheded
10.5120/7079-9312
PDF

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
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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Computer Science
Information Sciences
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Keywords

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

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