A probabilistic physics-guided framework for crack propagation prognostic

Fatigue crack is a critical failure mode in machinery elements, leading to numerous studies focused on understanding crack initiation and propagation mechanisms. Two main methods have been proposed in the literature for crack propagation prediction: data-driven and model-based methods. Data-driven methods are widely used for crack prognostic but demand extensive, high-quality datasets. In contrast, fatigue models can accurately predict component degradation and remaining life under cyclic loading if their parameters are well-aligned with the data. This work proposes a physics-guided framework to improve crack propagation prediction through the Paris law, even with limited datasets, using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method for model parameter estimation, along with data pre-processing and uncertainty quantification. The optimization was performed using weighted data, while data pre-processing incorporated outlier removal to enhance model reliability. When applying the weighting scheme, an improvement of about 15% in prediction accuracy was observed. Further improvements, along with the implementation of a two-stage outlier detection process and Monte Carlo simulations, resulted in a 20% improvement in accuracy. This study highlights that the integration of data pre-processing, parameter estimation with BFGS, and uncertainty quantification effectively tackles the challenge of limited observation data in crack prognostic, distinguishing it from prior approaches.