发表机构
School of Software, Beihang University; State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications; School of Computer and Artificial Intelligence, Beijing Technology and Business University（北京航空航天大学软件学院; 北京邮电大学网络与交换技术国家重点实验室; 北京工商大学计算机与人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对轴承RUL预测中深度学习模型可解释性不足的问题，提出物理增强双向多阶图融合网络，通过多阶图传播器等技术提升预测性能与可解释性，在公开数据集上误差最低且估计更保守。
AI 中文摘要
准确预测轴承剩余使用寿命（RUL）是智能维护的关键挑战。尽管基于深度学习的预测方法已展现出有效性，但现有方法在学习非线性轴承退化过程及模型可解释性方面仍存在局限，尤其是在工程应用中，深度学习模型的“黑箱”特性易引发对其可靠性的担忧。因此，本文提出一种用于可解释轴承RUL预测的物理增强双向多阶图融合网络。该网络从正向和反向退化序列中挖掘互补信息，具体而言，引入多阶图传播器以捕捉局部-全局退化依赖关系；进一步设计门控交叉融合机制，动态平衡正向与反向方向的特征贡献；还在动态内存中存储代表性历史退化原型，使最终RUL预测不再仅依赖当前潜在特征，而是受可复用的历史退化知识指导。为揭示模型学习非线性退化过程的方式，特征映射部分采用Kolmogorov-Arnold网络，该网络允许通过可学习函数对非线性映射进行可视化。最后，开发物理增强的动态损失函数，助力网络学习有效且可靠的退化表示。在两个公开数据集上开展的大量实验表明，所提方法误差最低，且相较于现有方法能提供更保守的估计。本文代码可在指定URL获取。
英文摘要
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.
