Abstract Positive-feedback mechanisms such as fatigue induce a self-accelerating behavior, captured by models displaying infinite limit-state asymptotics, collectively known as the failure forecast method (FFM). This paper presents a Bayesian model parameter estimation approach to the fully nonlinear FFM implementation and compares the results to the classic linear regression formulation, including a regression uncertainty model. This process is demonstrated in a cyclic loading fatigue crack propagation application, both on a synthetic data set and on a full fatigue experiment. A novel ”switch point” parameter is included in the Bayesian formulation to account for nonstationary changes in the growth parameter.

中文翻译：

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使用贝叶斯推理的故障预测方法的概率估计方法

摘要 疲劳等正反馈机制会引起自加速行为，由显示无限极限状态渐近线的模型捕获，统称为失效预测方法 (FFM)。本文介绍了完全非线性 FFM 实现的贝叶斯模型参数估计方法，并将结果与​​经典线性回归公式（包括回归不确定性模型）进行了比较。这个过程在循环加载疲劳裂纹扩展应用中得到了证明，包括合成数据集和完整的疲劳实验。贝叶斯公式中包含一个新的“切换点”参数，以解释增长参数的非平稳变化。