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  11. A Deep Learning-based Prognostic Approach For Predicting Turbofan Engine Degradation And Remaining Useful Life

A deep learning-based prognostic approach for predicting turbofan engine degradation and remaining useful life

Samiha M Elsherif1, Bassel Hafiz2, M A Makhlouf2

  • 1Information Systems Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, 41522, Egypt. Samiha_ahmed@ci.suez.edu.eg.

Scientific Reports
|July 19, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Predicting turbofan engine Remaining Useful Life (RUL) is crucial for aviation safety. A novel hybrid deep learning model, CAELSTM, significantly improves RUL prediction accuracy, enhancing prognostics and health management systems.

Area of Science:

  • Aerospace Engineering
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Component degradation in turbofan engines poses significant risks to aviation safety.
  • Accurate Remaining Useful Life (RUL) prediction is vital for effective prognostics and health management (PHM).
  • Existing methods require enhancement for improved RUL prediction accuracy.

Purpose of the Study:

  • To propose a novel deep learning model for accurate RUL prediction of turbofan engines.
  • To evaluate the proposed model's performance on the CMAPSS benchmark dataset (FD001 and FD003).
  • To demonstrate the model's superiority over existing state-of-the-art methods.

Main Methods:

  • A hybrid Convolutional Autoencoder and Attention-based LSTM (CAELSTM) model was developed.
Keywords:
C-MAPSSCNNLSTMPrognostics

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  • Piecewise linear degradation modeling and data preprocessing were applied to the CMAPSS dataset.
  • An autoencoder, attention-based LSTM, and fully connected layers were utilized for feature extraction and RUL prediction.

    • Main Results:

    • The CAELSTM model achieved superior RUL prediction performance on FD001 and FD003 sub-datasets.
    • Achieved Root Mean Square Error (RMSE) of 14.44 (FD001) and 13.40 (FD003).
    • Mean Absolute Error (MAE) of 10.49 (FD001) and 10.68 (FD003), and scores of 282.38 (FD001) and 264.47 (FD003) were recorded.

    Conclusions:

    • The proposed CAELSTM model demonstrates significant effectiveness and superiority for turbofan engine RUL prediction.
    • This advancement offers a dependable tool for predictive maintenance in aerospace, enhancing aviation safety.
    • The model shows great promise for improving prognostics and health management systems.
    Remaining useful life prediction