Subspace-based fault detection robust to changes in the noise covariances
作者： Michael Döhler , Laurent Mevel
DOI: 10.1016/J.AUTOMATICA.2013.06.019
关键词: Mathematics 、 Fault detection and isolation 、 Linear system 、 Algorithm 、 Subspace topology 、 Statistical hypothesis testing 、 Residual 、 Robustness (computer science) 、 Ambient noise level 、 Speech recognition 、 LTI system theory 、 Control and Systems Engineering 、 Electrical and Electronic Engineering
摘要: The detection of changes in the eigenstructure a linear time invariant system by means subspace-based residual function has been proposed previously. While enjoying some success its applicability particular context vibration monitoring, robustness this framework against noise properties not properly addressed yet. In paper, new robust is and statistics covariances shown. complete theory for hypothesis testing fault derived numerical illustration provided.
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