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dc.contributor.authorSalvador Palau, Aen
dc.contributor.authorDhada, Maharshien
dc.contributor.authorBakliwal, Ken
dc.contributor.authorParlikad, Ajithen
dc.date.accessioned2019-07-16T23:30:31Z
dc.date.available2019-07-16T23:30:31Z
dc.date.issued2019-10-01en
dc.identifier.issn0952-1976
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/294680
dc.description.abstractDespite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties of existing systems to adapt to the dynamic and heterogeneous properties of real asset fleets. In order to address this, we present an Industrial Multi Agent System for real-time distributed collaborative prognostics. Our system fufil ls all six core properties of Advanced Multi Agent Systems: Distribution, Flexibility, Adaptability, Scalability, Leanness, and Resilience. Experimental examples of each are provided for the case of prognostics using the C-MAPPS engine degradation data set, and data from a fleet of industrial gas turbines. Prognostics are performed using the Weibull Time To Event - Recurrent Neural Network algorithm. Collaboration is achieved by sharing information between agents in the system. We conclude that distributed collaborative prognostics is especially pertinent for systems with presence of sensor faults, limited computing capabilities or significant fleet heterogeneity.
dc.publisherElsevier Ltd.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleAn Industrial Multi Agent System for real-time distributed collaborative prognosticsen
dc.typeArticle
prism.endingPage606
prism.publicationDate2019en
prism.publicationNameEngineering Applications of Artificial Intelligenceen
prism.startingPage590
prism.volume85en
dc.identifier.doi10.17863/CAM.41785
dcterms.dateAccepted2019-07-11en
rioxxterms.versionofrecord10.1016/j.engappai.2019.07.013en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-10-01en
dc.contributor.orcidParlikad, Ajith [0000-0001-6214-1739]
dc.identifier.eissn1873-6769
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (645733)
pubs.funder-project-idEPSRC (via Lancaster University) (EP/R004935/1)
pubs.funder-project-idEPSRC (EP/I019308/1)
pubs.funder-project-idEPSRC (EP/K000314/1)
pubs.funder-project-idEPSRC (EP/L010917/1)
pubs.funder-project-idEPSRC (EP/N021614/1)
cam.orpheus.successThu Jan 30 10:42:10 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2020-10-01


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International