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We present a novel method for the identification of abnormal episodes in gas-turbine vibration data, in which we show 1) how a model of normal engine behaviour is constructed using signatures of "normal" engine vibration response; 2) how extreme value theory (EVT), a branch of statistics used to determine the expected value of extreme values drawn from a distribution, can be used to set novelty thresholds in the model, which, if exceeded, indicate an "abnormal" episode; 3) application to large data sets of modern gas-turbine flight data, which shows successful novelty detection results with low false-positive alarm rates. The advantages of this approach over previous work are 1) a very low false-positive alarm rate, while maintaining sufficient sensitivity to detect known abnormal events; 2) the use of a Bayesian framework such that uncertainty in the distribution of "normal" data is modelled, giving a principled, probabilistic interpretation of results; 3) an implementation that is sufficiently "lightweight" in processing and memory resources that real-time, on-line novelty detection is possible in an "on-wing" engine health-monitoring system. ©2008 IEEE.

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