Enhancement of Industrial Systems Reliability using Integrated Maintenance Model

Abstract

Using an integrated method of Particle Swarm Optimization (PSO), Corrective Maintenance (CM), and Preventive Maintenance (PM), this study created an optimal maintenance scheduling model for industrial systems. When the concept was applied to power generation equipment, it resulted in notable increases in cost savings, efficiency, and dependability. The Mean Time Between Failures (MTBF) of the Gas Turbine Generator (TG-001) increased from 350 to 370 hours, while the Mean Time to Repair (MTTR) decreased from 6 to 4 hours. Overall Equipment Efficiency (OEE) increased from 75% to 82%, while equipment availability increased from 87% to 91%. The monthly maintenance expenses dropped from ₦18,000,000 to ₦12,000,000, a 30% decrease. Corrective expenditures decreased from ₦12,625,000 to ₦10,500,000, while preventive maintenance costs were reduced from ₦8,750,000 to ₦6,750,000. The PSO algorithm stabilized costs at ₦14,000,000 after 50 iterations, reducing unplanned downtime and improving the distribution of resources. This study provides a valuable foundation for enhancing industrial efficiency and dependability by demonstrating the efficacy of switching to proactive maintenance techniques. This study significantly advances the fields of industrial systems optimization and maintenance management. Critical issues in increasing equipment dependability, decreasing downtime, and lowering maintenance costs were resolved by creating and implementing an integrated maintenance scheduling model in conjunction with Particle Swarm Optimization (PSO).