基于实测结构响应的结构劣化序贯贝叶斯诊断与预后
Sequential Bayesian diagnosis and prognosis of structural deterioration from measured structural responses
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中文总结 AI 辅助
本文提出一个序贯贝叶斯框架，通过扩展BUS方法实现基于实测响应的结构退化诊断、预后与可靠性预测，并在导管架结构疲劳退化案例中验证其有效性。
中文摘要 AI 辅助
退化结构的结构健康监测需要从实测结构响应中间接推断潜在退化过程，并将其传播到工程关注的量。本文提出一个概率框架，通过区分不确定退化和模型参数的贝叶斯推断与当前退化状态的诊断、未来演化的预后以及结构可靠性的预测，来形式化这一过程。退化模型由此提供了基于监测的推断与这些下游任务之间的共同概率联系。为了在监测信息累积时实现序贯推断，贝叶斯更新与结构可靠性方法（BUS）通过嵌套公式化与连续增长的监测数据集相关联的BUS事件而得到扩展。所得到的序贯BUS方法允许子集模拟从先前监测阶段获得的增广条件样本总体继续，而不是从先验分布重新开始。然后，后验总体可以传播到基于监测的可靠性分析。该框架在冗余导管架型结构的疲劳退化中得到验证，其中从模拟振动监测获得的模态特性用于推断焊接连接的高维退化模型。结果表明，连续的监测观测如何约束合理的退化历史，提供概率性的系统级诊断，并更新裂纹扩展预后和结构可靠性，同时保留产生相似结构响应的退化情景之间的模糊性。因此，所提出的框架提供了从基于响应的监测数据到退化诊断、预后和可靠性预测的连贯概率路径。
英文摘要
Structural health monitoring of deteriorating structures requires the latent deterioration process to be inferred indirectly from measured structural responses and propagated to quantities of engineering interest. This paper presents a probabilistic framework that formalizes this process by distinguishing Bayesian inference of uncertain deterioration and model parameters from diagnosis of the current deterioration state, prognosis of its future evolution, and prediction of structural reliability. The deterioration model thereby provides the common probabilistic link between monitoring-based inference and these downstream tasks. To enable sequential inference as monitoring information accumulates, Bayesian Updating with Structural Reliability Methods (BUS) is extended through a nested formulation of the BUS events associated with successively growing monitoring datasets. The resulting Sequential BUS method allows Subset Simulation to continue from the augmented conditional sample population obtained at the preceding monitoring stage rather than restarting from the prior distribution. The posterior population can then be propagated to monitoring-informed reliability analysis. The framework is demonstrated for fatigue deterioration of a redundant jacket-type structure, where modal properties obtained from simulated vibration monitoring are used to infer a high-dimensional deterioration model of welded connections. The results show how successive monitoring observations constrain plausible deterioration histories, provide probabilistic system-level diagnosis, and update crack-growth prognosis and structural reliability while retaining ambiguity between deterioration scenarios that produce similar structural responses. The proposed framework thus provides a coherent probabilistic route from response-based monitoring data to deterioration diagnosis, prognosis, and reliability prediction.
发表机构
- Bundesanstalt für Materialforschung und -prüfung (BAM)（联邦材料研究与测试研究所）
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