Abstract
We develop novelty detection techniques for the analysis of data from a large-vehicle engine turbocharger in order to illustrate how abnormal events of operational significance may be identified with respect to a model of normality. Results are validated using polynomial function modelling and reduced dimensionality visualisation techniques to show that system operation can be automatically classified into one of three distinct state spaces, each corresponding to a unique set of running conditions.
This classification is used to develop a regression algorithm that is able to predict the dynamical operating parameters of the turbocharger and allow the automatic detection of periods of abnormal operation. Visualisation of system trajectories in high-dimensional space are communicated to the user using parameterised projection techniques, allowing ease of interpretation of changes in system behaviour.
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Clifton, D.A., Bannister, P.R., Tarassenko, L. (2007). Novelty Detection in Large-Vehicle Turbocharger Operation. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_59
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DOI: https://doi.org/10.1007/978-3-540-73325-6_59
Publisher Name: Springer, Berlin, Heidelberg
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