Remaining useful life estimation of turbofan engines using adaptive fault detection learning

Abstract

Failure prognostics have greatly enhanced the predictive maintenance of industrial systems by providing the remaining useful life (RUL) information, offering opportunities for high reliability, availability, maintainability and safety. To do so, historical monitoring data are injected into machine learning model to learn how to predict the RUL and then, in an online phase directly estimate the RUL of a new similar system. However, in case of multiple degradation trends representing multiple systems, it lead to different times of anomaly appearance and therefore various RUL values for learning. This situation makes difficult to train the predictor and use in this case an approximated unique RUL value. Hence, this paper proposes an adaptive anomaly detection methodology to identify the times of fault occurrence, and then assign the correct RUL values of each failure trajectory to the train the predictor. This methodology will facilitate the learning task for an accurate prediction of system RUL. The performance of the proposed methodology is highlighted using a long short-term memory (LSTM) network with the accelerated run to failure data of turbofan engines provided by the NASA to estimate the RUL.