Purpose – 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. Design/methodology/approach – 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. Findings – 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. Originality/value – 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.

Defining a data-driven maintenance policy: an application to an oil refinery plant / Antomarioni, Sara; Bevilacqua, Maurizio; Potena, Domenico; Diamantini, Claudia. - In: INTERNATIONAL JOURNAL OF QUALITY AND RELIABILITY MANAGEMENT. - ISSN 0265-671X. - 36:1(2019), pp. 77-97. [10.1108/IJQRM-01-2018-0012]

Defining a data-driven maintenance policy: an application to an oil refinery plant

ANTOMARIONI, SARA
;
Maurizio Bevilacqua;Domenico Potena;Claudia Diamantini
2019-01-01

Abstract

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Originality/value – 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/262526
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