Publication
Evaluation Metrics for Collaborative Fault Detection and Diagnosis in Cyber-Physical Systems
| dc.contributor.author | Piardi, Luis | |
| dc.contributor.author | Oliveira, Andre Schneider | |
| dc.contributor.author | Costa, Pedro | |
| dc.contributor.author | Leitão, Paulo | |
| dc.date.accessioned | 2024-10-21T10:16:48Z | |
| dc.date.available | 2024-10-21T10:16:48Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Cyber-physical systems (CPS) rapidly expand within industrial contexts in a new era of digitalization, processing power, and inter-device communication capabilities. These advancements integrate technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud and edge computing, granting processes and operations a high degree of autonomy. In addition, these interconnections foster collective intelligence arising from information exchange and collaboration between components, often outperforming individual capabilities. This collective intelligence manifests in fault detection and diagnosis (FDD) tasks within CPS, as it significantly improves the flexibility, performance, and scalability. However, the inherent complexity of CPS poses challenges in determining the best configuration of the collaboration parameters, such as when and how to collaborate, wherein incorrect adjustments may lead to decision errors and compromise the system’s performance. With this in mind, this paper proposes seven metrics to evaluate collaboration performance for fault detection and diagnosis in multi-agent systems (MAS)-based CPS, evaluating when the collaboration is beneficial or when the collaboration parameters need to be adjusted. The experiments focus on collaborative fault detection in temperature and humidity sensors within warehouse racks, where the proposed evaluation metrics point out the impact of collaboration on the detection task, as well as possible actions to be adopted to improve the agent’s performance. | pt_PT |
| dc.description.sponsorship | This work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). Luis Piardi thank FCT for the PhD Grant UI/BD/151286/2021 (DOI: 10.54499/UI/BD/151286/2021). | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | |
| dc.identifier.citation | Piardi, Luis; Oliveira, André; Costa, Pedro; Leitão, Paulo (2024). Evaluation Metrics for Collaborative Fault Detection and Diagnosis in Cyber-Physical Systems. In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA). Padova: IEEE, p. 1-8. ISBN 979-8-3503-6123-0. | pt_PT |
| dc.identifier.doi | 10.1109/ETFA61755.2024.10710719 | pt_PT |
| dc.identifier.isbn | 979-8-3503-6123-0 | |
| dc.identifier.uri | http://hdl.handle.net/10198/30455 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Cyber-Physical System | pt_PT |
| dc.subject | Collaborative Fault Detection | pt_PT |
| dc.subject | Collaborative Fault Diagnosis | pt_PT |
| dc.subject | Multi-agent System | pt_PT |
| dc.title | Evaluation Metrics for Collaborative Fault Detection and Diagnosis in Cyber-Physical Systems | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT//UI%2FBD%2F151286%2F2021/PT | |
| oaire.citation.endPage | 8 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Piardi | |
| person.familyName | Leitão | |
| person.givenName | Luís | |
| person.givenName | Paulo | |
| person.identifier | A-8390-2011 | |
| person.identifier.ciencia-id | C51A-82DB-016F | |
| person.identifier.ciencia-id | 8316-8F13-DA71 | |
| person.identifier.orcid | 0000-0003-1627-8210 | |
| person.identifier.orcid | 0000-0002-2151-7944 | |
| person.identifier.scopus-author-id | 35584388900 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 643e9664-ec9b-4b2f-b93c-6f3f8335fd61 | |
| relation.isAuthorOfPublication | 68d9eb25-ad4f-439b-aeb2-35e8708644cc | |
| relation.isAuthorOfPublication.latestForDiscovery | 643e9664-ec9b-4b2f-b93c-6f3f8335fd61 | |
| relation.isProjectOfPublication | 6e01ddc8-6a82-4131-bca6-84789fa234bd | |
| relation.isProjectOfPublication | d0a17270-80a8-4985-9644-a04c2a9f2dff | |
| relation.isProjectOfPublication | 6255046e-bc79-4b82-8884-8b52074b4384 | |
| relation.isProjectOfPublication | eb3d92a9-fc78-486c-9101-5bd5ff389f92 | |
| relation.isProjectOfPublication.latestForDiscovery | d0a17270-80a8-4985-9644-a04c2a9f2dff |