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用于诊断问题的神经网络与故障概率评估

Neural networks and fault probability evaluation for diagnosis issues.

作者信息

Kourd Yahia, Lefebvre Dimitri, Guersi Noureddine

机构信息

Department of Control Engineering, University of Mohamed Khider, 07000 Biskra, Algeria.

Electrical Engineering and Automatic Control Research Group (GREAH), University of Le Havre, 25 rue Philippe Lebon, 76058 Le Havre, France.

出版信息

Comput Intell Neurosci. 2014;2014:370486. doi: 10.1155/2014/370486. Epub 2014 Jul 15.

DOI:10.1155/2014/370486
PMID:25132845
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4123600/
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.

摘要

本文提出了一种用于未知非线性系统故障检测与隔离的新型故障检测与隔离（FDI）技术。该研究的目的是通过人工智能和概率方法构建并分析残差。人工神经网络首先用于建模问题。神经网络模型旨在学习所考虑系统的无故障和有故障行为。一旦生成残差，就会对其应用概率准则评估，以确定一组候选故障中最可能的故障。该研究还包括比较这些工具的贡献及其局限性，特别是通过建立定量指标来评估其性能。根据置信因子的计算，所提出的方法适用于评估FDI决策的可靠性。该方法应用于检测和隔离DAMADICS基准中的19个候选故障。将所提方案获得的结果与根据常规阈值方法获得的结果进行比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/139e/4123600/d6c2f6b2b86c/CIN2014-370486.014.jpg
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