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A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
Linnaeus Univ, Dept Comp Sci & Media Technol, S-35195 Växjö, Sweden..
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Linnaeus Univ, Dept Comp Sci & Media Technol, S-35195 Växjö, Sweden.ORCID iD: 0000-0002-2833-7196
Univ Naples Federico II, Dept Elect Engn & Informat Technol, I-80125 Naples, Italy..
Linnaeus Univ, Dept Comp Sci & Media Technol, S-35195 Växjö, Sweden..
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 12887-12910Article, review/survey (Refereed) Published
Abstract [en]

The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2023. Vol. 11, p. 12887-12910
Keywords [en]
Maintenance engineering, Predictive maintenance, Prognostics and health management, Transfer learning, Artificial intelligence, Fault diagnosis, domain adaptation, fault detection, fault prognosis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-62029DOI: 10.1109/ACCESS.2023.3239784ISI: 000933765200001Scopus ID: 2-s2.0-85147287812OAI: oai:DiVA.org:mdh-62029DiVA, id: diva2:1742075
Available from: 2023-03-08 Created: 2023-03-08 Last updated: 2023-03-08Bibliographically approved

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Flammini, Francesco

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