Remaining Useful Life Estimation of Turbofan Engine Using LSTM Neural Networks

Remaining Useful Life (RUL) estimation plays a crucial role in Prognostics and Health Management of aircraft engines. Due to the complexity and nonlinearity of aircraft engine model and the development of data mining, data-driven approaches have been developed and applied in RUL estimation. However, traditional data-driven approaches such as regression methods and Multilayer Perceptrons (MLP) can't make use of sequential information. Sequence models such as Recurrent Neural Networks (RNN) have flaws when dealing with long-term dependencies. In this paper we propose a Long Short-Term Memory (LSTM) model for RUL estimation. Besides, we proposed a Euclidean distance-based method to identify the initial useful life to make the estimation more accurate.