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.
Remaining Useful Life Estimation of Turbofan Engine Using LSTM Neural Networks
01.08.2018
216477 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
An improved prognostics model with its application to the remaining useful life of turbofan engine
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