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Uncertainty-Aware Remaining Useful Life Prediction via Synergizing TCN-Transformer Networks and Fractional Brownian
Yiming Geng1, Tianshuo Yu2,3, Yan Liu2,3
1School of Communication Engineering, Jilin University, Changchun 130022, China.
Entropy (Basel, Switzerland)
|May 26, 2026
Summary
This study introduces an advanced prognostic framework for predicting equipment Remaining Useful Life (RUL). It enhances accuracy and quantifies uncertainties in mechanical degradation using TCN-Transformer and fractional Brownian motion.
Area of Science:
- Mechanical Engineering
- Artificial Intelligence
- Data Science
Background:
- Accurate Remaining Useful Life (RUL) prediction is crucial for intelligent operation and maintenance of high-precision equipment.
- Existing deep learning methods often provide point estimations and neglect non-Markovian characteristics and stochastic uncertainties in mechanical degradation.
Purpose of the Study:
- To propose a novel uncertainty-aware hybrid prognostic framework for RUL prediction.
- To address the limitations of existing methods by incorporating stochastic uncertainties and non-Markovian properties.
- To enhance the reliability and accuracy of prognostic predictions for complex degradation processes.
Main Methods:
- Developed a TCN-Transformer hybrid network to learn multi-scale drift functions, capturing localized causal features and global temporal dependencies.
- Utilized fractional Brownian motion (FBM) to model the diffusion process, accounting for long-range dependence and stochasticity in degradation.
- Derived an approximate analytical expression for the RUL probability density function (PDF) using the first hitting time (FHT) principle for robust uncertainty quantification.
Main Results:
- The proposed framework demonstrated superior predictive accuracy on the XJTU-SY bearing and servo tool holder datasets.
- Achieved reliable uncertainty quantification, providing a more comprehensive understanding of RUL predictions.
- Effectively captured both short-term causal features and long-term dependencies in degradation data.
Conclusions:
- The uncertainty-aware hybrid prognostic framework offers significant improvements over existing methods for RUL prediction.
- The integration of TCN-Transformer and FBM effectively models complex degradation dynamics and associated uncertainties.
- The method provides robust support for condition-based maintenance and intelligent decision-making in high-precision equipment.
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