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An Adaptive Framework for Remaining Useful Life Prediction Integrating Attention Mechanism and Deep Reinforcement

Yanhui Bai1,2, Jiajia Du1, Honghui Li1,2

  • 1School of Computer Science and Technology, Beijing Jiaotong University, Beijing100044, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
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Predicting Remaining Useful Life (RUL) is crucial for mechanical health. This study introduces ADAPT-RULNet, an adaptive framework using deep learning and reinforcement learning for accurate RUL prediction from heterogeneous sensor data.

Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Prognostics and Health Management (PHM)

Background:

  • Remaining Useful Life (RUL) prediction is vital for mechanical component health.
  • Current deep learning methods struggle with heterogeneous sensor data and individual differences.
  • Existing techniques often rely on unimodal data or static feature extraction.

Purpose of the Study:

  • To propose ADAPT-RULNet, an adaptive framework for accurate RUL prediction in mechanical components.
  • To overcome limitations of existing methods in handling complex operational conditions and sensor heterogeneity.
  • To achieve end-to-end optimization from raw data to RUL prediction using integrated DL and DRL.

Main Methods:

  • Functional Alignment Resampling (FAR) for signal generation.
Keywords:
Deep Deterministic Policy Gradient (DDPG)Functional Alignment Resampling (FAR)attention mechanismremaining useful life (RUL) prediction

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Last Updated: Jan 13, 2026

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  • Attention-enhanced Dynamic Time Warping (DTW) for individual degradation stage identification.
  • Hybrid multi-scale network with attention for feature extraction and Bayesian fusion.
  • Deep Deterministic Policy Gradient (DDPG) for adaptive parameter optimization.

    • Main Results:

    • ADAPT-RULNet demonstrated lower average Root Mean Square Error (RMSE).
    • The proposed model achieved higher prediction accuracy compared to existing approaches.
    • Evaluated successfully on aircraft engines and railway freight car wheels.

    Conclusions:

    • ADAPT-RULNet effectively captures individual differences and complex operational conditions.
    • The framework offers improved prediction accuracy and robustness for industrial applications.
    • Integration of DL and DRL provides adaptive optimization for RUL prediction.