Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0

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dc.contributor.authorDiez-Olivan, Alberto
dc.contributor.authorDel Ser, Javier
dc.contributor.authorGalar, Diego
dc.contributor.authorSierra, Basilio
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T12:07:05Z
dc.date.available2024-07-24T12:07:05Z
dc.date.issued2019-10
dc.descriptionPublisher Copyright: © 2018 Elsevier B.V.
dc.description.abstractThe so-called “smartization” of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field.en
dc.description.sponsorshipThe authors would like to thank the Basque Government for its funding support through the EMAITEK program.
dc.description.statusPeer reviewed
dc.format.extent20
dc.identifier.citationDiez-Olivan , A , Del Ser , J , Galar , D & Sierra , B 2019 , ' Data fusion and machine learning for industrial prognosis : Trends and perspectives towards Industry 4.0 ' , Information Fusion , vol. 50 , pp. 92-111 . https://doi.org/10.1016/j.inffus.2018.10.005
dc.identifier.doi10.1016/j.inffus.2018.10.005
dc.identifier.issn1566-2535
dc.identifier.urihttps://hdl.handle.net/11556/3728
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85055203735&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofInformation Fusion
dc.relation.projectIDEusko Jaurlaritza
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsData fusion
dc.subject.keywordsData-driven prognosis
dc.subject.keywordsIndustry 4.0
dc.subject.keywordsMachine learning
dc.subject.keywordsSoftware
dc.subject.keywordsSignal Processing
dc.subject.keywordsInformation Systems
dc.subject.keywordsHardware and Architecture
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.titleData fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0en
dc.typejournal article
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