A measurement-based fault detection approach applied to monitor robots swarm

Belkacem Khaldi; Fouzi Harrou; Ying Sun; Foudil Cherif

doi:10.1109/ICoSC.2017.7958703

A measurement-based fault detection approach applied to monitor robots swarm

Belkacem Khaldi, Fouzi Harrou, Ying Sun, Foudil Cherif

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

Original language	English (US)
Title of host publication	2017 6th International Conference on Systems and Control (ICSC)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	21-26
Number of pages	6
ISBN (Print)	9781509039609
DOIs	https://doi.org/10.1109/ICoSC.2017.7958703
State	Published - Jul 10 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Belkacem Khaldi and Foudil Cherif) would like to thank the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider,Biskra, Algeria for the continued support during the research.

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10.1109/ICoSC.2017.7958703

https://repository.kaust.edu.sa/bitstream/10754/625670/1/A%20measurement-based%20Fault%20Detection%20Approach%20Applied%20To%20Monitor%20Robots.pdf

Cite this

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title = "A measurement-based fault detection approach applied to monitor robots swarm",

abstract = "Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.",

author = "Belkacem Khaldi and Fouzi Harrou and Ying Sun and Foudil Cherif",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582 Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Belkacem Khaldi and Foudil Cherif) would like to thank the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider,Biskra, Algeria for the continued support during the research.",

year = "2017",

month = jul,

day = "10",

doi = "10.1109/ICoSC.2017.7958703",

language = "English (US)",

isbn = "9781509039609",

pages = "21--26",

booktitle = "2017 6th International Conference on Systems and Control (ICSC)",

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T1 - A measurement-based fault detection approach applied to monitor robots swarm

AU - Khaldi, Belkacem

AU - Harrou, Fouzi

AU - Sun, Ying

AU - Cherif, Foudil

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582 Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The authors (Belkacem Khaldi and Foudil Cherif) would like to thank the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider,Biskra, Algeria for the continued support during the research.

PY - 2017/7/10

Y1 - 2017/7/10

N2 - Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

AB - Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.

UR - http://hdl.handle.net/10754/625670

UR - http://ieeexplore.ieee.org/document/7958703/

UR - http://www.scopus.com/inward/record.url?scp=85025838967&partnerID=8YFLogxK

U2 - 10.1109/ICoSC.2017.7958703

DO - 10.1109/ICoSC.2017.7958703

M3 - Conference contribution

AN - SCOPUS:85025838967

SN - 9781509039609

SP - 21

EP - 26

BT - 2017 6th International Conference on Systems and Control (ICSC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

A measurement-based fault detection approach applied to monitor robots swarm

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