首页    期刊浏览 2024年12月03日 星期二
登录注册

文章基本信息

  • 标题:Asymptotic analysis of subspace-based data-driven residual for fault detection with uncertain reference ⁎
  • 本地全文:下载
  • 作者:Eva Viefhues ; Michael Döhler ; Falk Hille
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:24
  • 页码:414-419
  • DOI:10.1016/j.ifacol.2018.09.610
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThe local asymptotic approach is promising for vibration-based fault diagnosis when associated to a subspace-based residual function and efficient hypothesis testing tools. It has the ability of detecting small changes in some chosen system parameters. In the residual function, the left null space of the observability matrix associated to a reference model is confronted to the Hankel matrix of output covariances estimated from test data. When this left null space is not perfectly known from a model, it should be replaced by an estimate from data to avoid model errors in the residual computation. In this paper, the asymptotic distribution of the resulting data-driven residual is analyzed and its covariance is estimated, which includes also the covariance related to the reference null space estimate. The importance of including the covariance of the reference null space estimate is shown in a numerical study.
  • 关键词:KeywordsFault detectionuncertainty in referenceresidual evaluationstatistical testsvibration measurement
国家哲学社会科学文献中心版权所有