Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge Article Swipe
Oscar Serradilla
,
Ekhi Zugasti
,
Julian Ramirez de Okariz
,
Jon Rodriguez
,
Urko Zurutuza
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/0951192x.2022.2043562
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/0951192x.2022.2043562
The 4th industrial revolution has connected machines and industrial plants, facilitating process monitoring and the implementation of predictive maintenance (PdM) systems that can save up to 60% of maintenance costs. Nowadays, most PdM research is carried out with expert systems and data-driven algorithms, but it is mainly focused on improving the results of reference simulation data sets. Hence, industrial requirements are not commonly addressed, and there is no guiding methodology for their implementation in real PdM use-cases. The objective of this work is to present a methodology for PdM application in industrial companies by combining data-driven techniques with domain knowledge. It defines sequentially ordered stages, steps and tasks to facilitate the design, development and implementation of PdM systems according to business and process characteristics. It also facilitates the collaboration among the required working profiles and defines deliverables. It is designed in a flexible and iterative way, combining standards, state-of-the-art methodologies and referent works of the field. Finally, the proposed methodology is validated on two use-cases: a bushing testbed and a press machine of the production line. These use-cases aim to facilitate, guide and speed up the implementation of the methodology on other PdM use-cases.
Related Topics
Concepts
Testbed
Computer science
Process (computing)
Predictive maintenance
Field (mathematics)
Domain (mathematical analysis)
Systems engineering
Deliverable
Iterative and incremental development
Software engineering
Industrial engineering
Engineering
Reliability engineering
Computer network
Mathematical analysis
Pure mathematics
Operating system
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/0951192x.2022.2043562
- OA Status
- green
- Cited By
- 14
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214817260
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214817260Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/0951192x.2022.2043562Digital Object Identifier
- Title
-
Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledgeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-03-02Full publication date if available
- Authors
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Oscar Serradilla, Ekhi Zugasti, Julian Ramirez de Okariz, Jon Rodriguez, Urko ZurutuzaList of authors in order
- Landing page
-
https://doi.org/10.1080/0951192x.2022.2043562Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://ebiltegia.mondragon.edu/xmlui/bitstream/20.500.11984/5495/4/Methodology%20for%20data-driven%20predictive%20maintenance%20models%20design%2c%20development%20and%20implementation%20on%20manufacturing%20guided%20by%20domain%20knowledge.pdfDirect OA link when available
- Concepts
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Testbed, Computer science, Process (computing), Predictive maintenance, Field (mathematics), Domain (mathematical analysis), Systems engineering, Deliverable, Iterative and incremental development, Software engineering, Industrial engineering, Engineering, Reliability engineering, Computer network, Mathematical analysis, Pure mathematics, Operating system, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 6, 2023: 6Per-year citation counts (last 5 years)
- References (count)
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47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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