Novelty detection for the identification of abnormalities

The principle of novelty detection offers an approach to the problem of fault detection which only requires the normal class to be defined. A model of normality is learnt by including normal examples only in the training data; abnormalities are then identified by testing for novelty against this description. In this paper, we review our work on statistical models of normality in feature space and we explain how we have used novelty detection to identify unusual vibration signatures in jet engines. The main aim of the paper, however, is to introduce the concept of a neural network predictor as a model of normality. The neural network is trained to predict an output value given a set of input patterns, all of which are acquired during normal operation. In the application under consideration, we make use of the spatial correlations in the temperature profile of the exhaust gas from a turbine to predict a thermocouple reading given another part of the temperature profile and the engine speed. Abnormalities in test data are then identified by correspondingly high prediction errors from the model. The sensitivity of such a model to an incipient fault is demonstrated with thermocouple data recorded from a turbine operating in a remote location.