A physics-enhanced bidirectional multi-order graph fusion network for interpretable bearing remaining useful life prediction

2026-09-02

AI Summary

为解决轴承剩余使用寿命预测的非线性过程学习与模型可解释性问题，提出了一种物理增强双向多阶图融合网络方法。

Abstract

Accurate prediction of bearing remaining useful life (RUL) is a key challenge for intelligent maintenance. Although deep learning-based prediction methods have showed effectiveness, existing methods still have limitations in learning nonlinear bearing degradation processes and model interpretability. Especially in engineering applications, the "black box" nature of deep learning models can easily raise concerns about their reliability. Therefore, we propose a physics-enhanced bidirectional multi-order graph fusion network for interpretable bearing RUL prediction. Our network mines complementary information from both forward and backward degradation sequences. Specifically, our network introduces a multi-order graph propagator to capture the local-global degradation dependencies. A gated cross-fusion mechanism is further designed to dynamically balance the feature contributions from both forward and backward directions. Then, our network stores representative historical degradation prototypes in dynamic memory, so that the final RUL prediction no longer depends solely on the current latent features, but is guided by reusable historical degradation knowledge. To reveal how our model learns the nonlinear degradation process, the feature mapping parts utilize the Kolmogorov-Arnold network, which allows the nonlinear mapping to be visualized using learnable functions. Finally, a physics-enhanced dynamic loss function is developed to help our network learn effective and reliable degradation representations. Extensive experiments on two public datasets show that our method achieves the lowest error while providing more conservative estimates than existing methods. Our code is available at https://github.com/IMGresearcher/PE-BMGN.

Problem

• bearing remaining useful life
• nonlinear degradation
• model interpretability
• deep learning

Innovation

• physics-enhanced
• bidirectional multi-order graph fusion
• interpretable RUL prediction
• gated cross-fusion mechanism
• dynamic memory