神经网络与粒子滤波相结合的裂纹扩展预测框架
Neural Network and Particle Filtering: A Hybrid Framework for Crack Propagation Prediction
摘要：
裂纹检测、长度估计和剩余使用寿命（RUL）预测是可靠性工程中研究最多的课题。一些研究工作已经研究了不同材料的失效物理学（PoF），以及数据驱动的方法作为传统PoF研究的替代方案。为了弥合这两种技术之间的差距，我们提出了一种新的混合框架疲劳裂纹长度估计和预测。断裂力学退化数据进行基于物理的建模，估计参数的巴黎定律，包括相关的不确定性。裂纹长度估计推断通过馈送手动提取的功能，从超声波信号的神经网络（NN）。然后使用粒子滤波（PF）方法进行裂纹长度预测，该方法将巴黎定律作为移动函数，并使用NN的输出作为观测值来更新裂纹扩展路径。这种混合框架结合了机器学习，基于物理的建模和贝叶斯更新，具有良好的效果。
英文摘要：
Crack detection, length estimation, and Remaining Useful Life (RUL) prediction are among the most studied topics in reliability engineering. Several research efforts have studied physics of failure (PoF) of different materials, along with data-driven approaches as an alternative to the traditional PoF studies. To bridge the gap between these two techniques, we propose a novel hybrid framework for fatigue crack length estimation and prediction. Physics-based modeling is performed on the fracture mechanics degradation data by estimating parameters of the Paris Law, including the associated uncertainties. Crack length estimations are inferred by feeding manually extracted features from ultrasonic signals to a Neural Network (NN). The crack length prediction is then performed using the Particle Filter (PF) approach, which takes the Paris Law as a move function and uses the NN's output as observation to update the crack growth path. This hybrid framework combines machine learning, physics-based modeling, and Bayesian updating with promising results.
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