Neural Networks and Fault Probability Evaluation for Diagnosis Issues
Joint Authors
Kourd, Yahia
Lefebvre, Dimitri
Guersi, Noureddine
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-15
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract 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.
American Psychological Association (APA)
Kourd, Yahia& Lefebvre, Dimitri& Guersi, Noureddine. 2014. Neural Networks and Fault Probability Evaluation for Diagnosis Issues. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-466661
Modern Language Association (MLA)
Kourd, Yahia…[et al.]. Neural Networks and Fault Probability Evaluation for Diagnosis Issues. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-15.
https://search.emarefa.net/detail/BIM-466661
American Medical Association (AMA)
Kourd, Yahia& Lefebvre, Dimitri& Guersi, Noureddine. Neural Networks and Fault Probability Evaluation for Diagnosis Issues. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-466661
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-466661