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- W4288061775 abstract "In this letter, a novel data-based fault diagnosis framework for the linear process is developed using dissipativity shaping method. First, the dissipativity for finite-horizon trajectory data is learnt and shaped such that it is valid for <bold xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>all but one</b> certain fault (to be identified). Then, using the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>weaving response lemma</i> , the data-based dissipativity for fault diagnosis purposes is extended over an arbitrarily long horizon. Next, from the available input-output trajectories, the corresponding fault-selective coefficient matrices of the dissipativity condition of the process are shaped using semi-definite programming, which are further fed into the online diagnosis algorithm. By checking the dissipation rate against the threshold with process input-output trajectories, the fault can be diagnosed. Finally, the presented method is illustrated through an example of a heat exchanger." @default.
- W4288061775 created "2022-07-28" @default.
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- W4288061775 date "2023-01-01" @default.
- W4288061775 modified "2023-09-30" @default.
- W4288061775 title "Data-Based Fault Diagnosis via Dissipativity-Shaping" @default.
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- W4288061775 doi "https://doi.org/10.1109/lcsys.2022.3193978" @default.
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