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

ABSTRACT

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.

KEYWORDS:

• Predictive maintenance
• methodology
• data-driven
• domain knowledge
• manufacturing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Availability of data and material

For the research of this work, only publicly available studies, works and references combined with authors’ experience have been used.

Additional information

Funding

Oscar Serradilla, Ekhi Zugasti and Urko Zurutuza are part of the Intelligent Systems for Industrial Systems research group of Mondragon Unibertsitatea (IT1357-19), supported by the Department of Education, Universities and Research of the Basque Country. This work was partially supported by the European Union’s Horizon 2020 research and innovation programme’s project QU4LITY under Grant agreement with European Union’s Horizon 2020 research and innovation programme European Union’s Horizon 2020 research and innovation programme 825030 and Provincial Council of Gipuzkoa’s project MEANER under Grant agreement FA/OF326/2020(ES).