Application of an Intuitive Novelty Metric for Jet Engine Condition Monitoring

Application of novelty detection to a new class of jet engine is considered within this paper, providing a worked example of the steps necessary for constructing a model of normality. Abnormal jet engine vibration signatures are automatically identified with respect to a training set of normal examples. Pre-processing steps suitable for this area of application are investigated. An intuitive metric for assigning novelty scores to patterns is introduced, with benefits for reducing model sensitivity to noise, and in pruning patterns from the model training set.