Abstract
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 Failure-based 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.
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