Suresh Perinpanayagam

Biography

Dr Suresh Perinpanayagam graduated with a Master of Engineering from Imperial College London in 1996. He first started work at the Ford Motor Company Development Centres in Dunton, UK and Merknick, Germany to implement an integrated engineering platform for new vehicle development. This platform saved millions of pounds in cost for vehicle development and eventually established the company as an agile establishment. After 3 years, Suresh returned to Imperial College London to complete his PhD. This was done at the Rolls-Royce University Technology Centre at Imperial College, under the auspices of the FP7 programme. Suresh worked with major engine manufacturers in Europe (Rolls-Royce, UK; MTU, Germany; SNECMA, France). After graduating in 2004, Suresh worked as a Associate Research Scientist at the Singapore Institute of Manufacturing Technology (SIMTech), which is an A*STAR (Agency for Science, Technology and Research) research institute in Singapore. Suresh was one of the pioneering researchers who was awarded $1 million project funded by Boeing, EADS and Singapore's Science and Engineering Research Council (SERC) under the auspices of the SERC Aerospace Research Programme. The project looked at developing novel sensors and signal-processing techniques for the detection of defects on composites of thickness of more than one inch, such as those on the Boeing 787 skins. Suresh jointly collaborated with Imperial College London, UK, Uppsala University, Sweden and Nanyang Technological University, Singapore. Suresh managed fifteen people in this project which involved developing existing staff to work on the project and recruiting new staff, training them on the research activities, industrial engagement and focusing them on the business deliverables.

Research Interest

Dr Perinpanayagam has research expertise in the following key areas: Sensor networks for system health monitoring, Technologies for Adaptive Intelligent systems: data-mining, artificial intelligence, diagnostics, prognostics, self-healing, Knowledge representation and reasoning: logic and probabilistic reasoning, reasoning for spatial-temporal phenomena and non-monotonic reasoning, Real-time hybrid (model-based and data-driven) prognostic models for system health management and Dealing with uncertainty: resilient approaches for recognising, dealing with, and learning from errors and inconsistent sensor data. Sensor fusion, data fusion and information fusion.