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The Importance of Using Domain Knowledge When Designing and Implementing Data-Driven Decision Models for Maintenance: Insights from Industrial Cases
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-0729-0122
Volvo Construction Equipment Operations Eskilstuna, Eskilstuna, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-4543-0069
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-7494-1474
2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 601-614Conference paper, Published paper (Refereed)
Abstract [en]

The advanced technologies available in the development of Smart Maintenance within Industry 4.0 have the potential to significantly improve the efficiency of industrial maintenance. However, it is important to be careful when deciding which technologies to implement for a given application and when evaluating the quality of the data generated. Otherwise, what should be cost-effective solutions may end up being cost-driving. The use of domain knowledge in selecting, developing, implementing, setting up, and utilizing these technologies is increasingly important for achieving success. In this paper, we will elaborate on this topic by presenting and analyzing insights from industrial cases, drawing on the authors’ extensive experience in the field.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2024. p. 601-614
Keywords [en]
Data-driven decisions, Domain knowledge, Industrial cases, Maintenance, Smart maintenance technologies, Cost effectiveness, Advanced technology, Cost-effective solutions, Data driven decision, Decision modeling, Industrial case, Industrial maintenance, IS costs, Maintenance technologies, Smart maintenance technology
National Category
Reliability and Maintenance
Identifiers
URN: urn:nbn:se:mdh:diva-65367DOI: 10.1007/978-3-031-39619-9_44Scopus ID: 2-s2.0-85181979974ISBN: 9783031396182 (print)OAI: oai:DiVA.org:mdh-65367DiVA, id: diva2:1828610
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved

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Bengtsson, MarcusGiliyana, SanSalonen, Antti

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