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Perspectives on Smart Maintenance Technologies – A Case Study in Large Manufacturing Companies
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Mälardalen Industrial Technology Center AB, Sweden.ORCID iD: 0000-0002-4543-0069
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-7494-1474
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation. Volvo Construction Equipment, Sweden.ORCID iD: 0000-0002-0729-0122
2022 (English)In: Advances in Transdisciplinary Engineering / [ed] Amos H.C. Ng, Anna Syberfeldt, Dan Högberg, Magnus Holm, IOS Press, 2022, Vol. 21, p. 255-266Conference paper, Published paper (Refereed)
Abstract [en]

The manufacturing industry faces significant technical challenges due to the industry 4.0 technologies, which play an essential role in maintenance development. Maintenance in industry 4.0, also named smart maintenance, maintenance 4.0, predictive maintenance, etc., is boosted using industry 4.0 technologies, such as Industrial Internet of Things (IIoT), Big Data and Analytics, Cloud Computing, Augmented Reality (AR), Additive Manufacturing (AM), etc. Previous research presents several smart maintenance technologies, but the manufacturing industry still finds it challenging to implement the technologies cost-effectively. One problem is that there is insufficient research on how smart maintenance technologies can be implemented cost-effectively and add value to the manufacturing industry. Therefore, this paper aims to explore perspectives on smart maintenance technologies: 1) if there are any implemented smart maintenance technologies, 2) in what context, 3) added values, 4) challenges, 5) opportunities, 6) advantages, and 7) disadvantages with the technologies. This paper presents the results of a case study based on an online open questionnaire with respondents working in maintenance organizations in large manufacturing companies. 

Place, publisher, year, edition, pages
IOS Press, 2022. Vol. 21, p. 255-266
Keywords [en]
Smart Maintenance, Maintenance 4.0, Predictive Maintenance, Industry 4.0
National Category
Engineering and Technology
Research subject
Innovation and Design
Identifiers
URN: urn:nbn:se:mdh:diva-58298DOI: 10.3233/ATDE220145Scopus ID: 2-s2.0-85132808966ISBN: 978-1-64368-268-6 (print)ISBN: 978-1-64368-269-3 (electronic)OAI: oai:DiVA.org:mdh-58298DiVA, id: diva2:1661137
Conference
SPS2022, Proceedings of the 10th Swedish Production Symposium, Skövde, Sweden, 26-29 April 2022
Available from: 2022-05-25 Created: 2022-05-25 Last updated: 2023-11-13Bibliographically approved
In thesis
1. Smart Maintenance Technologies in the Manufacturing Industry: Implementation, Challenges, Enablers and Benefits
Open this publication in new window or tab >>Smart Maintenance Technologies in the Manufacturing Industry: Implementation, Challenges, Enablers and Benefits
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In Industry 4.0, production, Information Technology (IT), and the Internet are combined. The nine technologies of Industry 4.0, Artificial Intelligence (AI) and Cyber-Physical System (CPS), are changing machines, strategies, processes, and maintenance.

In the first generation of maintenance, machines were run to failure, which is related to Corrective Maintenance. Systems for planning and control were implemented in the second generation, related to Predetermined Maintenance. Condition Based Maintenance (CBM) was presented in the third maintenance generation. Industry 4.0 places new demands on maintenance and different maintenance approaches are presented in previous research, such as Maintenance 4.0, Smart Maintenance and Self-Maintenance. This research focuses on smart maintenance technologies, using the nine technologies of Industry 4.0, such as Industrial Internet of Things (IIoT), and Big Data and Analytics, for machine connection, maintenance data collection, analysis of data, and making decisions using AI. CPS can be used to integrate the physical world, such as manufacturing machines, factory environment, material, people, and executions, with the cyber world, such as data analysis, apps, services, and decision-making.

Previous research presents several approaches to smart maintenance technologies. One problem is a lack of research regarding how smart maintenance technologies can be implemented to add benefits to the maintenance organization in line with company’s goal. Furthermore, previous research presents that further research is needed to support the manufacturing industry in what step an organization should take to implement smart maintenance technologies. In this research, four studies have been performed, which include literature reviews to obtain a clear overview of the research area of smart maintenance, as well as collected empirical data. The empirical data is collected from large companies and Small and Medium-sized Enterprises (SMEs), within the manufacturing industry, to obtain a clear overview of the manufacturing industry’ situation. The studies show that the manufacturing industry faces several challenges when implementing smart maintenance technologies, despite the concept of Industry 4.0 has been discussed for more than ten years. In this research, a conceptual implementation process is proposed, including challenges and enablers to consider when implementing smart maintenance technologies, as well as benefits of using smart maintenance technologies.

Place, publisher, year, edition, pages
Eskilstuna: Mälardalens universitet, 2023. p. 75
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 350
National Category
Engineering and Technology Computer Systems
Research subject
Industrial Systems
Identifiers
urn:nbn:se:mdh:diva-64731 (URN)978-91-7485-622-4 (ISBN)
Presentation
2023-12-15, Mälardalen Industrial Technology Center, John Engellaus Gata 1, Eskilstuna, 09:00 (Swedish)
Opponent
Supervisors
Available from: 2023-11-14 Created: 2023-11-13 Last updated: 2023-11-24Bibliographically approved

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Giliyana, SanSalonen, AnttiBengtsson, Marcus

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