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Probabilistic fatigue life prediction using a hierarchical Bayesian physics-informed neural network
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DOI：10.1007/s00158-026-04379-7.png)
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Abstract
En 中文
Existing probabilistic fatigue life prediction approaches using probabilistic stress-life (P-S-N) curves frequently rely on predefined fatigue life distributions, which introduces distributional bias and fails to capture the fatigue life uncertainty. Moreover, they often fail to keep physical consistency and quantify uncertainty across the entire survival-probability interval, especially under limited-sample or censored conditions. These limitations result in either overly conservative or insufficiently safe designs, posing persistent challenges in fatigue-related reliability-based design. To address these challenges, this study proposes a probabilistic fatigue life prediction method using a hierarchical Bayesian physics-informed neural network (HB-PINN). Key contributions include the following: (1) An adaptive uncertainty quantification strategy is proposed for unbiased life-distribution estimation. (2) A physics-guided standard deviation of the predicted fatigue life with credibility-weighted loss ensures accurate fatigue life scatter predictions under limited data. (3) By combining Bayesian inference with physics-constrained composite loss, the HB-PINN learns from failure and runout data to yield predictive fatigue life distributions. Validation on four fatigue datasets and wind turbine cases demonstrates that the proposed method can generate probabilistically accurate and physically consistent P-S-N curves across all reliability levels, enabling designs that satisfy various reliability requirements while avoiding overly conservative results.
Keywords:
Probabilistic fatigue life
P-S-N curve
Bayesian
Physics-informed neural network
Uncertainty quantification
Reliability-based design
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