用于基于总体的结构健康监测中环境与操作变量的同时识别与去除的潜变量模型
Latent variable models for simultaneous EOV identification and removal in population-based SHM
查看机构详情
- University of Sheffield（谢菲尔德大学）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
该研究针对基于总体的结构健康监测中未测量的环境与操作变量（EOV）问题，提出将潜EOV建模为状态空间高斯过程的层次贝叶斯识别框架，实现了EOV与损伤的区分，在基准结构和海上风电场场景中均表现出优于传统方法的性能。
中文摘要 AI 辅助
对环境与操作变量（EOV）的鲁棒处理是基于总体的结构健康监测（PBSHM）中的一项开放性挑战。当EOV信号未被测量时，该问题的难度会进一步增加。传统结构健康监测中常用的方法是应用基于投影的方法，该方法会丢弃健康特征数据的子空间，其依据是EOV信号主导了测量特征的方差。然而，基于投影的方法存在一个常见缺陷：当损伤作用于方差主导方向附近时，损伤敏感性会与EOV一同被去除。针对特定未测量EOV的去除，另一种可采用的识别假设是其缓慢性，即潜EOV过程具有长时相关性。本文将潜EOV建模为状态空间高斯过程，可通过卡尔曼滤波器实现计算复杂度为O(T)的易处理推理。研究开发了一种鲁棒的层次贝叶斯识别框架，该框架利用拉普拉斯近似实现对潜EOV和无EOV残差特征的总体级识别。该方法首先在文献中一个受热EOV作用的实验室规模基准结构上进行验证，结果表明其具有鲁棒的损伤检测和EOV恢复能力。随后，该方法被应用于一个带有交错部署和损伤的模拟九涡轮海上风电场，在匹配的假阳性率下，该方法相较于基于投影和协整的基准方法实现了显著的真阳性提升。
英文摘要
The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM). The difficulty is compounded in the case that the EOV signals are unmeasured. A common approach in conventional SHM is to apply \emph{projection-based} methods that discard subspaces of healthy feature data, reasoning that the EOV signal dominates the variance of the measured features. However, a common pitfall of projection-based approaches is that when damage acts close to the same variance-dominant direction, damage sensitivity is removed along with the EOV. An alternative identifying assumption for the removal of particular unmeasured EOVs is slowness; the latent EOV process is characterised by its long temporal correlation. In this paper, the latent EOV is cast as a state-space Gaussian process, enabling tractable $\mathcal{O}(T)$ inference via a Kalman filter. A robust hierarchical Bayesian identification framework is developed that enables population-level identification of latent EOVs and EOV-free residual features, using a Laplace approximation. The approach is first validated on a single laboratory-scale benchmark structure from the literature, subject to thermal EOVs, demonstrating robust damage detection and EOV recovery. The method is then applied to a simulated nine-turbine offshore wind farm with staggered deployment and damage, where it delivers a substantial true-positive uplift over projection and cointegration-based baselines at matched false-positive rates.
