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    Monitoring robotic swarm systems under noisy conditions using an effective fault detection strategy

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    Type
    Article
    Authors
    Harrou, Fouzi cc
    Khaldi, Belkacem
    Sun, Ying cc
    Cherif, Foudil
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2018-10-22
    Online Publication Date
    2018-10-22
    Print Publication Date
    2019-02-01
    Permanent link to this record
    http://hdl.handle.net/10754/629958
    
    Metadata
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    Abstract
    Fault detection in robotic swarm systems is imperative to guarantee their reliability, safety, and to maximize operating efficiency and avoid expensive maintenance. However, data from these systems are generally contaminated with noise, which masks important features in the data and degrades the fault detection capability. This paper introduces an effective fault detection approach against noise and uncertainties in data, which integrates the multiresolution representation of data using wavelets with the sensitivity to small changes of an exponentially weighted moving average scheme. Specifically, to monitor swarm robotics systems performing a virtual viscoelastic control model for circle formation task, the proposed scheme has been applied to the uncorrelated residuals form principal component analysis model. A simulated data from ARGoS simulator is used to evaluate the effectiveness of the proposed method. Also, we compared the performance of the proposed approach to that of the conventional principal component-based approach and found improved sensitivity to faults and robustness to noises. For all the fault types tested–abrupt faults, random walks, and complete stop faults–our approach resulted in a significant enhancement in fault detection compared with the conventional approach.
    Citation
    Harrou F, Khaldi B, Sun Y, Cherif F (2018) Monitoring robotic swarm systems under noisy conditions using an effective fault detection strategy. IEEE Sensors Journal: 1–1. Available: http://dx.doi.org/10.1109/JSEN.2018.2877183.
    Sponsors
    The work presented in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Sensors Journal
    DOI
    10.1109/JSEN.2018.2877183
    Additional Links
    https://ieeexplore.ieee.org/document/8501946
    ae974a485f413a2113503eed53cd6c53
    10.1109/JSEN.2018.2877183
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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