TY - GEN A1 - Mevel, L. T1 - Asymptotic analysis of subspace-based datadriven residual for fault detection with unceratin reference N2 - The 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. T2 - 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2018 CY - Warsaw, Poland DA - 29.08.2018 KW - Fault detection KW - Uncertainty in reference KW - Residual evaluation KW - Statistical tests KW - Vibration measurement PY - 2018 UR - https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/46505 AN - OPUS4-46505 AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany