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.
Subject
Strategy and Management,General Business, Management and Accounting
Reference60 articles.
1. Data mining for subassembly selection;Journal of Manufacturing Science and Engineering,2004
2. Agrawal, R., Imieliński, T. and Swami, A. (1993), “Mining association rules between sets of items in large databases”, ACM Sigmod Record, ACM, Vol. 22 No. 2, pp. 207-216.
3. Enabling predictive maintenance strategy in rail sector: a clustering approach;Wseas Transactions On Computers,2014
4. A maintenance prediction system using data mining techniques;World Congress on Engineering,2012
5. EXPERT-MM: a knowledge-based system for maintenance management. Artificial Intelligence in Engineering,1993
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