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Neural Networks and Fault Probability Evaluation for Diagnosis Issues

Identifieur interne : 000330 ( Main/Exploration ); précédent : 000329; suivant : 000331

Neural Networks and Fault Probability Evaluation for Diagnosis Issues

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

Source :

RBID : PMC:4123600

English descriptors

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.


Url:
DOI: 10.1155/2014/370486
PubMed: 25132845
PubMed Central: 4123600


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<p>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.</p>
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HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i   -Sk "pubmed:25132845" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd   \
       | NlmPubMed2Wicri -a LeHavreV1 

Wicri

This area was generated with Dilib version V0.6.25.
Data generation: Sat Dec 3 14:37:02 2016. Site generation: Tue Mar 5 08:25:07 2024