Keith’s research is concerned with applications of advanced signal processing and machine learning methods to structural dynamics. The primary application is in the aerospace industry, although there has also been interaction with ground transport and offshore industries.
One of the research themes concerns non-linear systems. The research conducted here is concerned with assessing the importance of non-linear modelling within a given context and formulating appropriate methods of analysis. The analysis of non-linear systems can range from the fairly pragmatic to the extremes of mathematical complexity. The emphasis within the research group here is on the pragmatic and every attempt is made to maintain contact with engineering necessity.
Another major activity within the research group concerns structural health monitoring for aerospace systems and structures. The research is concerned with developing automated systems for inspection and diagnosis, with a view to reducing the cost-of-ownership of these high integrity structures.
The methods used are largely adapted from pattern recognition and machine learning; often the algorithms make use of biological concepts e.g. neural networks, genetic algorithms and ant-colony metaphors. The experimental approaches developed range from global inspection using vibration analysis to local monitoring using ultrasound. A major recent development is in ‘population-based structural health monitoring.