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    Compressive Sensing in Fault Detection

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    Author
    Farokhi, F; Shames, I
    Date
    2018-08-09
    Source Title
    Proceedings of the ... American Control Conference. American Control Conference
    Publisher
    IEEE
    University of Melbourne Author/s
    Shames, Iman; Farokhi, Farhad
    Affiliation
    Electrical and Electronic Engineering
    Metadata
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    Document Type
    Conference Paper
    Citations
    Farokhi, F. & Shames, I. (2018). Compressive Sensing in Fault Detection. Proceedings of the American Control Conference, 2018-June, pp.159-164. IEEE. https://doi.org/10.23919/ACC.2018.8431017.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/251420
    DOI
    10.23919/ACC.2018.8431017
    ARC Grant code
    ARC/DP170104099
    Abstract
    Randomly generated tests are used to identify faulty sensors in large-scale discrete-time linear time-invariant dynamical systems with high probability. It is proved that the number of the required tests for successfully identifying the location of the faulty sensors (with high probability) scales logarithmically with the number of the sensors and quadratically with the maximum number of faulty sensors. It is also proved that the problem of decoding the identity of the faulty sensors based on the random tests can be cast as a linear programming problem and therefore can be solved reliably and efficiently even for large-scale systems. A numerical example based on automated irrigation networks is utilized to demonstrate the results.

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