Electrical systems in large-scale refinery plants are critical for ensuring uninterrupted operation, safety, and productivity. However, due to the complexity and interdependence of these systems, traditional preventive maintenance strategies often fail to identify latent failures, optimize asset life cycles, or adapt to evolving operational demands. This study proposes a Reliability-Centered Maintenance (RCM) framework tailored to the specific needs of electrical infrastructure within high-risk industrial environments. The model integrates failure modes and effects analysis (FMEA), condition-based monitoring, and Bayesian risk modeling to assess component reliability, prioritize maintenance tasks, and reduce system downtime. A case study was conducted using operational data from a major petroleum refinery in the U.S. Gulf Coast region, involving 12 critical subsystems across three production units. The proposed RCM model was benchmarked against existing time-based maintenance (TBM) protocols. Results demonstrate that the RCM framework reduced unscheduled outages by 31%, improved mean time between failures (MTBF) by 22%, and achieved a 15% reduction in maintenance costs. The model also supported dynamic maintenance planning through probabilistic risk assessment and fault tree diagnostics. These findings underscore the value of implementing a structured, data-informed maintenance strategy that aligns system reliability with operational performance in refinery environments.
Reliability-Centered Maintenance, electrical systems, refinery plant, failure modes and effects analysis, Bayesian risk modeling, condition-based monitoring, maintenance optimization, industrial reliability.
IRE Journals:
Gbenga Rasheed Ajenifuja
"Reliability-Centered Maintenance (RCM) Model for Electrical Systems in Large-Scale Refinery Plants" Iconic Research And Engineering Journals Volume 8 Issue 1 2024 Page 743-752
IEEE:
Gbenga Rasheed Ajenifuja
"Reliability-Centered Maintenance (RCM) Model for Electrical Systems in Large-Scale Refinery Plants" Iconic Research And Engineering Journals, 8(1)