Remaining useful life (RUL) prediction has great significance in reducing operating costs and enhancing the maintainability and safety of rolling bearings. Recently, significant progress has been achieved in this field by leveraging deep learning approaches. However, advanced deep learning methods suffer from a black-box nature that makes them lack interpretability. Moreover, the prediction results may sometimes not follow laws of physics. To tackle this drawback, a physics-guided degradation trajectory modeling method is proposed for RUL prediction of rolling bearings, where physical knowledge is embedded in input preparation, model construction, and output specification. Specifically, phase space reconstruction is leveraged to construct the physics-guided inputs, transforming the degradation prediction of rolling bearings into the variation estimation of phase space trajectories. The degradation trajectory prediction strategy is derived from the physics-guided inputs and the decreased value of RUL is predicted through a compact one-dimensional convolutional neural network with the nonnegative bounded function. Simulation studies are performed with a theoretical model of defective rolling bearings to illustrate the effectiveness of the constructed physics-guided inputs. In addition, comparative analyses with advanced RUL prediction approaches under unseen working conditions and across different machines are conducted. The results demonstrate that the proposed method outperforms other approaches in both accuracy and robustness, showing its superior ability in RUL prediction of rolling bearings.

中文翻译：

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用于滚动轴承剩余使用寿命预测的物理引导退化轨迹建模


剩余使用寿命 （RUL） 预测对于降低运营成本、提高滚动轴承的可维护性和安全性具有重要意义。最近，通过利用深度学习方法，该领域取得了重大进展。然而，高级深度学习方法存在黑盒性质，使其缺乏可解释性。此外，预测结果有时可能不遵循物理定律。为了解决这一缺点，提出了一种物理引导的退化轨迹建模方法，用于滚动轴承的 RUL 预测，其中物理知识嵌入到输入准备、模型构建和输出规范中。具体来说，利用相空间重建来构建物理引导的输入，将滚动轴承的退化预测转化为相空间轨迹的变化估计。退化轨迹预测策略源自物理引导的输入，并通过具有非负有界函数的紧凑一维卷积神经网络预测 RUL 的降低值。使用有缺陷的滚动轴承的理论模型进行仿真研究，以说明构建的物理引导输入的有效性。此外，还在看不见的工作条件下和不同机器之间使用先进的 RUL 预测方法进行了比较分析。结果表明，所提方法在准确性和鲁棒性方面均优于其他方法，显示出其在滚动轴承 RUL 预测方面的卓越能力。