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.


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    Title :

    Remaining Useful Life Estimation of Turbofan Engine Using LSTM Neural Networks


    Contributors:
    Lan, Guoxing (author) / Li, Qing (author) / Cheng, Nong (author)


    Publication date :

    2018-08-01


    Size :

    216477 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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