Continual Learning for anomaly detection on turbomachinery prototypes - A real application

Abstract

We apply a Recurrent Neural Network (RNN), Kullback-Leibler (KL) divergence and a continual learning approach to check the status of several hundreds of sensors during turbo-machinery prototype testing. Turbo-machinery prototypes can be instrumented with up to thousands of sensors. Therefore, checking the health of each sensor is a time consuming activity. Prototypes are also tested on several different and a-priori unknown operating conditions, so we cannot apply a purely supervised model to detect potential anomalies of sensors and, moreover, we have to take into account a covariate shift because measurements drift continuously day by day. We continuously train a RNN (daily) to build a virtual sensor from other sensors and we compare the predicted signal vs the real signal to raise (in case) an anomaly. Furthermore, KL is used to estimate the overlap between the input distributions available at training time and the ones seen at test time, and thus the confidence level of the prediction. Finally we implement an end-to-end system to automatically train and evaluate the models. The paper presents the system and reports the application to a test campaign of about five hundred sensors.