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Neural networks and fault probability evaluation for diagnosis issues.

Identifieur interne : 000131 ( PubMed/Checkpoint ); précédent : 000130; suivant : 000132

Neural networks and fault probability evaluation for diagnosis issues.

Auteurs : Yahia Kourd [Algérie] ; Dimitri Lefebvre [France] ; Noureddine Guersi [Algérie]

Source :

Computational intelligence and neuroscience [ 1687-5273 ] ; 2014.

RBID : pubmed:25132845

English descriptors

KwdEn :
- Algorithms, Computer Simulation, Humans, Learning, Models, Neurological, Neural Networks (Computer), Nonlinear Dynamics, Probability, Signal Detection, Psychological, Time Factors.
MESH :
- Algorithms, Computer Simulation, Humans, Learning, Models, Neurological, Neural Networks (Computer), Nonlinear Dynamics, Probability, Signal Detection, Psychological, Time Factors.

Abstract

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

DOI: 10.1155/2014/370486
PubMed: 25132845

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.</div>
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<Abstract><AbstractText>This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.</AbstractText>
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Neural networks and fault probability evaluation for diagnosis issues.

Neural networks and fault probability evaluation for diagnosis issues.

Source :

English descriptors

Abstract

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