https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Testbed for Smart Maintenance Technologies
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: 0000-0002-0729-0122
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-7494-1474
Show others and affiliations
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Industry 4.0 presents nine technologies including Industrial Internet of Things (IIoT), Big Data and Analytics, Augmented Reality (AR), etc. Some of the technologies play an important role in the development of smart maintenance technologies. Previous research presents several technologies for smart maintenance. However, one problem is that the manufacturing industry still finds it challenging to implement smart maintenance technologies in a value-adding way. Open questionnaires and interviews have been used to collect information about the current needs of the manufacturing industry. Both the empirical findings of this paper, as well as previous research, show that knowledge is the most common challenge when implementing new technologies. Therefore, in this paper, we develop and present a testbed for how to approach smart maintenance technologies and to share technical knowledge to the manufacturing industry.

Keywords [en]
Smart Maintenance Technologies, Knowledge, Testbed
National Category
Engineering and Technology Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Systems
Identifiers
URN: urn:nbn:se:mdh:diva-64729OAI: oai:DiVA.org:mdh-64729DiVA, id: diva2:1811379
Available from: 2023-11-13 Created: 2023-11-13 Last updated: 2023-12-11Bibliographically 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

Open Access in DiVA

No full text in DiVA

Authority records

Giliyana, SanBengtsson, MarcusSalonen, Antti

Search in DiVA

By author/editor
Giliyana, SanBengtsson, MarcusSalonen, Antti
By organisation
Innovation and Product Realisation
Engineering and TechnologyProduction Engineering, Human Work Science and Ergonomics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 67 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf