Design of Prognostics and Health Management System for Turbofan Engine

Song, Han Qiang; Li, Ben Wei; Sun, Tao; Wang, Yong Hua

doi:10.4028/www.scientific.net/amm.599-601.2108

Cited by 3 publications

References 7 publications

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Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

2020

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Focusing on the fact that the existing research on optimal maintenance decision for remaining useful lifetime (RUL) prediction and imperfect maintenance has low accuracy of RUL prediction and rationality of decision results, an optimal maintenance decision method based on RUL prediction for the equipment subject to imperfect maintenance is proposed in this paper. Firstly, the nonlinear Wiener process is used to characterize the degradation law of the equipment. Secondly, the imperfect maintenance model that meets the upper limit of the maintenance number is established based on the nonhomogeneous Poisson process. Then, based on the concept of the first hitting time, the probability density function (PDF) of the RUL is derived. Finally, based on the RUL prediction results, the optimal maintenance decision model for the equipment subject imperfect maintenance is constructed. Through the example verification and cost parameter sensitivity analysis, the proposed method can effectively improve the accuracy of the RUL prediction and the scientific of maintenance decision results, which has engineering application value.

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Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

2020

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Case study of aeroengine parameter prediction based on MIV and ELM

Wang

Sun

et al. 2019

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

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Aeroengine Gas Path Parameter Trend Prediction Based on LSTM

Liu,

Chen,

Lin

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">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.</div></div>

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Design of Prognostics and Health Management System for Turbofan Engine

Cited by 3 publications

References 7 publications

Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

Case study of aeroengine parameter prediction based on MIV and ELM

Aeroengine Gas Path Parameter Trend Prediction Based on LSTM

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