Health Monitoring and Diagnostics

The availability of machines and plants is an essential requirement for productivity. In order to minimise unplanned downtimes, it is necessary to detect the sources of error at an early stage. Our research is focused on developing algorithms and techniques that continuously monitor complex systems to detect/predict abnormal system behaviour. We specially develop model-based and AI-based algorithms for condition monitoring of industrial gas turbines to enable condition-based maintenance scheduling as well as prediction of emerging faults.

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School of Engineering, College of Health and Science
University of Lincoln, Isaac Newton Building, Brayford Pool, University of, Lincoln LN6 7TS