Rate My Professor Suresh Perinpanayagam

Suresh Perinpanayagam is Professor of Engineering in the School of Physics, Engineering and Technology at the University of York, holding the specific title of Professor of Digital Engineering & AI. He obtained a Bachelor and Master of Aeronautical Engineering and a PhD in Mechanical Engineering from Imperial College London, with his PhD conducted at the Rolls-Royce Vibration University Technology Centre. A Chartered Engineer, his career began at the Ford Motor Company Development Centre in Dunton, UK, where he implemented data-centric platforms for new vehicle development. He led transformative research in Singapore on a S$1.2 million project funded by The Boeing Company, EADS, and Singapore's Science and Engineering Research Council (SERC) under the Aerospace Research Programme. Perinpanayagam previously held the Clough Chair Professorship in Artificial Intelligence for Predictive Maintenance at Charles Darwin University, Australia; worked at the SMART Centre on Future Urban Mobility, a collaboration between the Massachusetts Institute of Technology (MIT) and Singapore's National Research Foundation (NRF); and at Cranfield University, led a research group at the Integrated Vehicle Health Management (IVHM) Centre. There, he served as Principal Investigator for Data-Centric Engineering, AI, and Digital Twin research in collaboration with Airbus, and for the OLLGA project funded by Safran/ATI.

His research specializations encompass digital and data-centric engineering, digital twins, artificial intelligence, advanced modelling and simulation, data analytics for next-generation systems including fusion energy, electric and hydrogen aircraft, and autonomous transport vehicles. Key areas include prognostics and health management (PHM), predictive maintenance, condition monitoring, fault detection, Industry 4.0 applications for oil and gas, aircraft health monitoring systems, non-destructive testing for aircraft maintenance, additive manufacturing, physics of failure (PoF), and power electronics reliability. He received the Best Paper Award for 'Realising Duality Principle for Prognostic Models' at the 9th International Multi-Conference on Computing in the Global Information Technology (2014). Influential publications include 'The present and future of additive manufacturing in the aerospace sector: A review of important aspects' (2015, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 743 citations); 'A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery' (2016, Advances in Mechanical Engineering, 371 citations); 'Digital twin in aerospace industry: A gentle introduction' (2021, IEEE Access, 358 citations); 'A road map for reliable power electronics for more electric aircraft' (2021, Progress in Aerospace Sciences, 90 citations); and recent works such as 'Physics-augmented neural controlled differential equations for lithium-ion battery state-of-health prediction under missing cycling data' (2026, Journal of Power Sources) and 'Digital Twins in Fusion Energy Research: Current State and Future Directions' (2025, IEEE Access). With over 2,900 citations on Google Scholar, his work has substantial impact on intelligent systems, predictive analytics, and machine learning in engineering.