Abstract
In modern industries, equipment reliability and operational efficiency are critical for competitive advantage. Predictive maintenance (PdM) and reliability engineering optimization have emerged as key strategies to enhance equipment uptime, reduce maintenance costs, and prevent catastrophic failures. PdM leverages advanced data analytics, machine learning, and condition monitoring to forecast potential failures before they occur, allowing proactive maintenance interventions. Reliability engineering complements this by optimizing system design, components’ lifespan, and maintenance schedules. This review paper explores the evolution of predictive maintenance strategies, advanced diagnostic tools, reliability-centered approaches, and optimization techniques that improve maintenance decision-making. Various case studies from manufacturing, aerospace, and energy sectors are discussed. The paper also highlights the integration of Industry 4.0 technologies such as IoT, big data, and artificial intelligence in predictive maintenance. Challenges, future directions, and practical implications for industrial applications are also presented.
Keywords: Predictive Maintenance, Reliability Engineering, Optimization, Condition Monitoring, Machine Learning, Industry 4.0
Full Issue
| View or download the full issue | PDF 84-96 |