System and method for detecting anomalous system behaviour

Abstract

A method and apparatus for predicting failure of an engineering asset based on real-time data. The system comprises a plurality of sensors for measuring data on the engineering asset. The method of predicting failure comprises receiving a data record comprising data on the engineering asset collected from the plurality of sensors at time t, generating, using a trained machine learning algorithm, a probability Pthat the received data record indicates that the engineering asset is in a faulty state; determining what number of data records received in a look-back time Lare indicative of the engineering asset being in a faulty state, wherein the look-back time Lis a time period occurring before the time t at which the data record was collected; and predicting a probability of the engineering asset failing during a horizon time H, wherein the horizon time His a time period after time t at which the data record was collected. The predicting step implements a Bayes forecasting model to predict the probability of failure based on the generated probability Pand the number of data records which were determined to be faulty within the look-back time L.