Violeta I. McLoone

Papers

Total Citations

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About

Violeta I. McLoone’s research focuses on the intersection of machine learning and mechanical reliability, with a particular emphasis on predictive maintenance for rotating machinery. Her most-cited work, "Wear State Estimation of Rolling Element Bearings using Support Vector Machines" (2020), addresses a critical challenge in electric and rotating machines across transport, energy systems, and Industry 4.0 applications. By applying support vector machines to estimate bearing wear states, McLoone provides a data-driven approach to preempt catastrophic failures, enhancing system reliability and reducing downtime. This contribution, while still early in its citation trajectory with 4 citations, underscores her potential impact in a field where failure prevention is paramount. Her work bridges advanced computational methods with practical engineering needs, offering a pathway to more resilient industrial systems. McLoone’s research is particularly relevant for students and researchers in condition monitoring and smart manufacturing, as it demonstrates how machine learning can transform traditional maintenance strategies into proactive, intelligent solutions.