In this article, a hybrid network framework based on the empirical-mode decomposition improved by cubic spline interpolation (CSI-EMD) and double-channel multilayer feature fusion network (DCM-FFN) has been proposed to improve the accuracy of remaining useful life (RUL) prediction. The CSI-EMD is an empirical-mode decomposition (EMD) method that we have improved, which decomposes the multisensor time series into a bunch of intrinsic-mode functions, and then the DCM-FFN predicts the concrete states and summarizes the final RUL prediction value. Our proposed CSI-EMD method successfully alleviates the endpoint effect problem in the traditional EMD methods. In order to improve the ability of neural network to extract degraded signals, a method combining multiscale convolutional neural networks and echo state network is adopted in the framework. The proposed approach is evaluated by aircraft turbine engine data from NASA (FD001–FD004). Compared with the existing state-of-the-art methods, the root-mean-square error and score of the proposed method decreased by 15.33% and 54.86%, respectively. Therefore, results and comparisons show that the prediction performance of the proposed method has been improved greatly.


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    Title :

    Turbofan Engine's RUL Prediction Based on the CSI-EMD and Double-Channel Multilayer Feature Fusion Network


    Contributors:
    Zhou, Hongping (author) / Wu, Qingquan (author) / Peng, Peng (author) / Guo, Zhongyi (author)


    Publication date :

    2024-10-01


    Size :

    3509601 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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