Abstract

Purpose: Energy sector facilities (including oil & gas installations, power generation plants, and renewable energy farms) face high operational costs and risks if maintenance is suboptimal. This paper articulates the need for an integrated maintenance methodology uniting predictive, preventive, and corrective strategies. We address how siloed maintenance approaches limit reliability and propose a unified framework to improve performance. 
Design/Methodology/Approach: We develop a decision-support framework that bridges predictive, preventive, and corrective maintenance. The framework leverages real-time condition monitoring and predictive analytics to inform optimized preventive maintenance schedules, with corrective maintenance as a controlled fallback. A mixed-methods research design is used: quantitative reliability modeling (e.g., Weibull analysis, Markov chains) and a qualitative case study from an energy facility demonstrate the framework. Data sources include industry reliability databases and operational logs from a representative energy facility. Key performance indicators (KPIs) such as downtime, cost, Mean Time Between Failures (MTBF), and Mean Time to Repair (MTTR) are evaluated before and after integration. 
Findings/Results: The integrated maintenance approach is expected to reduce unplanned downtime and optimize maintenance costs significantly. By unifying strategies, the case application showed downtime reduction on the order of 40–60% and maintenance cost savings of ~20% compared to traditional single-strategy approaches. Reliability indices improved (e.g., MTBF increased by ~50%), and overall asset availability rose by several percentage points. These results highlight improvements in operational reliability, cost-efficiency, and safety performance under the integrated framework. 
Originality/Value: This work is novel in unifying predictive, preventive, and corrective maintenance into a single cohesive methodology for the energy sector. Prior studies primarily examine these strategies in isolation; our integrated decision-support framework provides a holistic approach aligned with asset management best practices. The paper’s value lies in demonstrating that synergistically combining maintenance strategies leads to superior long-term outcomes (reduced failures, optimized costs, and enhanced safety) for energy facilities. This integrated paradigm advances reliability-centered maintenance theory by bridging fragmented models and can inform both practitioners and researchers in maintenance engineering.