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Novelty detection is the identification of abnormal system behaviour, in which a model of normality is constructed, with deviations from the model identified as "abnormal". Complex high-integrity systems typically operate normally for the majority of their service lives, and so examples of abnormal data may be rare in comparison to the amount of available normal data. Given the complexity of such systems, the number of possible failure modes is large, many of which may not be characterised sufficiently to construct a. traditional multi-class classifier [22]. Thus, novelty detection is particularly suited to such cases, which allows previously-unseen or poorly-understood modes of failure to be correctly identified. This chapter describes recent advances in the application of novelty detection techniques to the analysis of data from gas-turbine engines.Whole-engine vibration-based analysis will be illustrated, using data measured from casemounted sensors, followed by the application of similar techniques to the combustor component. In each case, the investigation described by this chapter shows how advances in prognostic condition monitoring are being made possible in a principled manner using novelty detection techniques. © 2008 Springer-Verlag Berlin Heidelberg.

Original publication




Journal article


Studies in Computational Intelligence

Publication Date





269 - 296