The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements.
The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns.
The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested.
This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.
Antomarioni, S., Bevilacqua, M., Potena, D. and Diamantini, C. (2019), "Defining a data-driven maintenance policy: an application to an oil refinery plant", International Journal of Quality & Reliability Management, Vol. 36 No. 1, pp. 77-97. https://doi.org/10.1108/IJQRM-01-2018-0012Download as .RIS
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