Eoghan T. Chelmiah

Papers

Total Citations

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About

Eoghan T. Chelmiah’s research focuses on the intersection of mechanical reliability and machine learning, with a particular emphasis on predictive maintenance for rotating machinery. His most cited work, “Wear State Estimation of Rolling Element Bearings using Support Vector Machines” (2020, 4 citations), addresses a critical challenge in modern engineering: the early detection of bearing degradation in electric and rotating machines. By applying support vector machines to estimate wear states, Chelmiah’s approach offers a data-driven pathway to preventing catastrophic failures in transport, energy systems, and advanced robotics—key domains for Industry 4.0. Though his citation count is modest, the work’s practical relevance to high-stakes applications highlights its foundational value. Chelmiah’s contribution lies in bridging traditional mechanical diagnostics with intelligent algorithms, providing a framework for real-time condition monitoring that enhances system reliability and reduces downtime. His research is particularly notable for targeting the failure modes that pose the greatest risk to operational safety and efficiency, marking him as a promising voice in the growing field of prognostics and health management.