TY  - JOUR
AB  - 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.
VL  - 36
IS  - 1
SN  - 0265-671X
DO  - 10.1108/IJQRM-01-2018-0012
UR  - https://doi.org/10.1108/IJQRM-01-2018-0012
AU  - Antomarioni Sara
AU  - Bevilacqua Maurizio
AU  - Potena Domenico
AU  - Diamantini Claudia
PY  - 2018
Y1  - 2018/01/01
TI  - Defining a data-driven maintenance policy: an application to an oil refinery plant
T2  - International Journal of Quality & Reliability Management
PB  - Emerald Publishing Limited
SP  - 77
EP  - 97
Y2  - 2024/04/27
ER  -