A Robust Bayesian Framework for Degradation State Identification in the Presence of Outliers
提出一个贝叶斯在线退化状态估计框架，用学生t分布处理测量异常值，用变分贝叶斯量化参数不确定性，实现实时更新和剩余寿命预测，对预测性维护有用。
ABSTRACT Accurate degradation state estimation is critical for predictive maintenance, yet it is often compromised by measurement outliers and parameter uncertainty. Existing methods either assume Gaussian measurement errors, which are sensitive to outliers, or overlook parameter uncertainty, leading to overconfident predictions. To address these challenges, we propose a Bayesian online degradation state estimation framework that integrates robust error modeling with parameter uncertainty quantification. Specifically, we model measurement errors using a Student's‐ distribution to handle outliers and employ variational Bayes with Laplace and Gamma approximations to estimate posterior distributions of degradation states and parameters efficiently. This framework enables real‐time updates, ensuring adaptability to dynamic operating conditions. Furthermore, based on the estimated degradation states, we derive real‐time remaining useful life predictions and dynamic maintenance strategies under a cost function model. Numerical experiments and case studies demonstrate the framework's robustness, computational efficiency, and practical applicability.