TY - JOUR
T1 - Monitoring robotic swarm systems under noisy conditions using an effective fault detection strategy
AU - Harrou, Fouzi
AU - Khaldi, Belkacem
AU - Sun, Ying
AU - Cherif, Foudil
N1 - KAUST Repository Item: Exported on 2021-02-19
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: 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.
PY - 2018/10/22
Y1 - 2018/10/22
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/629958
UR - https://ieeexplore.ieee.org/document/8501946
UR - http://www.scopus.com/inward/record.url?scp=85055196981&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2018.2877183
DO - 10.1109/JSEN.2018.2877183
M3 - Article
AN - SCOPUS:85055196981
VL - 19
SP - 1141
EP - 1152
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
IS - 3
ER -