Novelty detection in jet engines

Neural network classifiers can be trained to estimate the posterior probability of a fault occurring given the values of a set of input parameters. With jet engines, however, faults are extremely rare and hence their prior probability is very low. The principle of novelty detection offers an alternative approach to the problem of fault detection. Novelty detection only requires the normal class to be defined. A statistical description of normality is learnt by including normal examples only in the training data; abnormalities are then identified by testing for novelty against this description. A real advantage of novelty detection is that anomalies which have not previously been seen will also be highlighted