Accurately predicting the trend of aero-engine gas path parameters is crucial for ensuring safe flight and enabling condition-based maintenance. However, the demanding and uncertain service environment introduces challenges in dealing with the noisy and non-stationary data collected by engine gas path sensors. Traditional time series models struggle to accurately predicts parameter trends, resulting in insufficient fitting and prediction accuracy. In this paper, we address these challenges by leveraging the characteristics of engine post-flight data and introducing Long Short-Term Memory (LSTM), a type of artificial neural network in deep learning. We construct both single-feature input and multi-feature input LSTM prediction models for six key indicators of engine gas path performance. We analyze the models' capabilities for single-step and multistep predictions. To evaluate the effectiveness of our approach, we compare the LSTM model with the traditional Autoregressive Moving Average (ARMA) model and support vector regression (SVR) method. The results demonstrate that the LSTM model outperforms the traditional ARMA and SVR models in terms of prediction accuracy and stability. This indicates that utilizing LSTM is an effective approach for improving the accuracy of engine gas path parameter prediction. By accurately predicting these parameters, we can enhance flight safety and enable more efficient condition based maintenance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Aeroengine Gas Path Parameter Trend Prediction Based on LSTM


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Zhou, Qifeng (Autor:in) / Chen, Xiaonan (Autor:in) / Qing, Xinlin (Autor:in) / Wang, Yi-shou (Autor:in) / Liu, Yuan (Autor:in) / Lin, Zhou (Autor:in)

    Kongress:

    SAE 2023 Intelligent Urban Air Mobility Symposium ; 2023



    Erscheinungsdatum :

    2023-12-31




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




    Dynamic Fault Diagnosis of Aeroengine Control System Sensors Based on LSTM-CNN

    Li, Huihui / Gou, Linfeng / Li, Huacong et al. | IEEE | 2023


    Prediction of Aeroengine Flight Load Spectrum

    Song, Y. | British Library Online Contents | 1997


    Particle-based Pedestrian Path Prediction using LSTM-MDL Models

    Hug, Ronny / Becker, Stefan / Htibner, Wolfgang et al. | IEEE | 2018


    Prediction of Noise Source for an Aeroengine Combustor

    Liu, Yu / Dowling, Ann / Swaminathan, Nedunchezhian et al. | AIAA | 2011


    Prediction of Combustion Noise for an Aeroengine Combustor

    Liu, Yu / Dowling, Ann P. / Swaminathan, Nedunchezhian et al. | AIAA | 2013