首页    期刊浏览 2024年07月08日 星期一
登录注册

文章基本信息

  • 标题:Neural Networks and Fault Probability Evaluation for Diagnosis Issues
  • 本地全文:下载
  • 作者:Yahia Kourd ; Dimitri Lefebvre ; Noureddine Guersi
  • 期刊名称:Computational Intelligence and Neuroscience
  • 印刷版ISSN:1687-5265
  • 电子版ISSN:1687-5273
  • 出版年度:2014
  • 卷号:2014
  • DOI:10.1155/2014/370486
  • 出版社:Hindawi Publishing Corporation
  • 摘要: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.
国家哲学社会科学文献中心版权所有