Data-Efficient and Uncertainty-Aware RUL Prediction Using Physics-Informed Neural Networks: Application to Degraded Rubber Components

Abstract

This study presents a prognostic framework that integrates Physics-Informed Neural Network (PINN) with uncertainty quantification (UQ) techniques to enable probabilistic prediction of the Remaining Useful Life (RUL) of rubber components subjected to degradation. The framework utilizes data acquired from thermal Highly Accelerated Life Testing (HALT), replicating long-term material aging behavior under elevated temperature conditions within a shortened time frame. To address the high cost and time consumption of HALT experiments, the proposed approach aims to ensure accurate and reliable predictions even with limited data availability. An empirical degradation model is embedded within the PINN structure, enabling physically consistent and data-efficient estimation of degradation model parameters. The framework employs uncertainty quantification techniques based on Bayesian inference, in which data-driven approaches (e.g., Gaussian Process modeling, Bayesian neural networks) and physics-based methods (e.g., Markov chain Monte Carlo, particle filtering) are separately applied to quantify variations arising from material properties, experimental conditions, and measurement noise. These methods generate posterior distributions from which failure time and probabilistic RUL estimates are derived based on a predefined degradation threshold. Compared to deterministic optimization methods, the proposed approach improves prediction robustness and interpretability, offering a cost-effective and scalable solution for prognostic modeling in engineering systems.