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Data Fusion and Machine Learning for Industrial Prognosis: Trends and Perspectives towards Industry 4.0
TECNALIA, Donostia-San Sebastián, Spain.
TECNALIA, Donostia-San Sebastián, Spain. Department of Communications Engineering, University of the Basque Country, Bilbao, Spain. Basque Center for Applied Mathematics (BCAM), Bilbao, Bizkaia, Spain.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik. TECNALIA, Donostia-San Sebastián, Spain.ORCID-id: 0000-0002-4107-0991
Department of Computer Sciences and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebastián, Spain.
2018 (engelsk)Inngår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 50, s. 92-111Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The 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.

sted, utgiver, år, opplag, sider
Elsevier, 2018. Vol. 50, s. 92-111
Emneord [en]
Data-driven prognosis, Data Fusion, Machine learning, Industry 4.0
HSV kategori
Forskningsprogram
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-71259DOI: 10.1016/j.inffus.2018.10.005ISI: 000466056900008Scopus ID: 2-s2.0-85055203735OAI: oai:DiVA.org:ltu-71259DiVA, id: diva2:1256915
Merknad

Validerad;2018;Nivå 2;2018-10-31 (svasva)

Tilgjengelig fra: 2018-10-18 Laget: 2018-10-18 Sist oppdatert: 2019-06-19bibliografisk kontrollert

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