Maintenance of Large Engineering Systems

Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledgeThe 4th industrial revolution has connected machines and industrial plants, facilitating process monitoring and the implementation of predictive maintenance systems (PdM) 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 datasets.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 usecases: a bushing testbed and a press machine of the production line. These usecases aim to facilitate, guide, and speed up the implementation of the methodology on other PdM use-cases.

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Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge

Serradilla

Zugasti

Okariz

et al. 2022

International Journal of Computer Integrated Manufacturing

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A review of rail track degradation prediction models

Falamarzi

Moridpour

Nazem

2019

Australian Journal of Civil Engineering

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Maintenance of Large Engineering Systems

Cited by 2 publications

References 19 publications

Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge

Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge

A review of rail track degradation prediction models

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