A Joint Long Short-Term Memory and AdaBoost regression approach with application to remaining useful life estimation

Abstract Along with wide application of sensors, multi-dimensional time-series data are commonly available for remaining useful life (RUL) estimation. This paper proposes a joint data-driven approach that adapts two models, AdaBoost regression and Long Short-Term Memory (LSTM), to estimate the RUL based on data trajectory extension. In RUL prediction, the data trajectories in the training set contain the data up to the units’ failure while the data trajectories in the testing set do not. Although this fact has a significant negative effect on the accuracy of RUL estimation, it is considered by few literatures. The proposed approach adapts the LSTM to learn the time series dependencies of training data and then extend the trajectories of testing data, aiming at reducing the variance of the lengths of data trajectory between the training and testing sets. Then, the proposed approach adapts the AdaBoost regression to estimate the RUL using the extended time series data. The proposed approach is competitive with state-of-the-art methods by demonstrating on two degradation datasets.