Predicting the remaining life of fatigue cracks is crucial for planning maintenance and repair strategies to prevent untoward incidents. This paper proposes a novel physics-informed neural network (PINN) method for identifying parameters and predicting remaining fatigue crack growth life (FCGL). Initially, the relationship between crack length and fatigue cycles is established through a neural network, and the gradient of fatigue cycles with respect to crack length is obtained by automatic differentiation. Subsequently, a composite loss function is designed to incorporate this gradient within the confines of physical knowledge, ensuring that the established relationship not only aligns with observed data but also adheres to physical knowledge. Furthermore, during the network training, the parameters in physical models are simultaneously updated to better conform to the individuality of the monitored subject. All predicted remaining FCGLs fall within the 1.5 times error band. Compared to purely data-driven or physics-based methods, the proposed method offers more robust and accurate predictions of remaining FCGLs.

中文翻译：

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一种基于物理信息的神经网络方法，用于识别参数并预测疲劳裂纹扩展的剩余寿命


预测疲劳裂纹的剩余寿命对于规划维护和维修策略以防止意外事故至关重要。本文提出了一种新的物理信息神经网络 （PINN） 方法，用于识别参数和预测剩余疲劳裂纹扩展寿命 （FCGL）。最初，通过神经网络建立裂纹长度和疲劳循环之间的关系，并通过自动微分获得疲劳循环相对于裂纹长度的梯度。随后，设计了一个复合损失函数，将这个梯度纳入物理知识的范围内，确保建立的关系不仅与观察到的数据一致，而且也符合物理知识。此外，在网络训练期间，物理模型中的参数会同时更新，以更好地符合被监测对象的个体性。所有预测的剩余 FCGL 都在 1.5 倍误差范围内。与纯数据驱动或基于物理的方法相比，所提出的方法对剩余的 FCGL 提供了更稳健和准确的预测。