title: Neural networks and fault probability evaluation for diagnosis issues.
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.
authors.affiliation: Department of Control Engineering, University of Mohamed Khider, 07000 Biskra, Algeria.
authors.forename: Yahia
authors.identifier: 0000-0002-4370-5700
authors.initials: Y
authors.lastname: Kourd
authors.affiliation: Electrical Engineering and Automatic Control Research Group (GREAH), University of Le Havre, 25 rue Philippe Lebon, 76058 Le Havre, France.
authors.forename: Dimitri
authors.initials: D
authors.lastname: Lefebvre
authors.affiliation: Department of Electronics, University of Badji Mokhtar, 23000 Annaba, Algeria.
authors.forename: Noureddine
authors.initials: N
authors.lastname: Guersi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011336:Probability; D017603:Signal Detection, Psychological; D013997:Time Factors