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Developing data-driven O&M policy through sequential pattern mining: A case study
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AI
DOI：10.1016/j.cie.2024.110318.png)
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Abstract
En 中文
This study focuses on utilizing data mining techniques to extract valuable insights from discrete industrial data, crucial for Operations and Maintenance (O&M) decision-making. We present an innovative methodology integrating data mining with the Plan-Do-Check-Act (PDCA) cycle and Knowledge Discovery in Databases (KDD) principles. Through a manufacturing facility case study, we develop a hybrid opportunistic O&M policy, blending Condition-based Maintenance (CBM) and Time-based Maintenance (TBM) to replace the traditional Failurebased Maintenance (FBM) strategy. Our proposed maintenance policy incorporates delay-time modeling in a machining center's critical sub-system, the lubrication equipment, proving its cost-effectiveness even amid parameter fluctuations. We assess the financial impact of adopting the Total Productive Maintenance (TPM) approach through Sensitivity Analysis, and compare it with established O&M policies, demonstrating the superior cost-effectiveness of our model.
Keywords:
Sequential patterns mining
Snapshot analysis
Opportunistic O & M policy
Maintenance
Data mining
Journal
IF:
6.5
Papers:
1.0W
Citations:
3.8W