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无监督的基于学习的异常检测用于桥梁结构健康监测:识别与正常结构行为的偏差

Jabez Nesackon Abraham¹, Minh Q Tran¹, Jerusha Samuel Jayaraj²

¹University of Minho, ISISE, ARISE, Department of Civil Engineering, 4800-058 Guimarães, Portugal.

Sensors (Basel, Switzerland)

|January 28, 2026

概括

本研究介绍了结构健康监测 (SHM) 的先进集合异常检测框架. 新方法可以更好地检测微妙的结构偏差,比现有技术提供更高的可靠性和稳定性.

科学领域:

土木工程土木工程是指土木工程.
数据科学数据科学数据科学
基础设施监测基础设施监测

背景情况:

结构健康监测 (SHM) 对民用基础设施的安全和维护至关重要.
无监督的异常检测对于识别没有标记损坏数据的结构偏差至关重要.
像累积距离参与因子 (CDPF) 和半参数极值理论 (SEVT) 这样的现有方法提供了基线,但却面临微妙的异常.

研究的目的:

为SHM开发一个改进的无监督异常检测框架.
解决基线方法在检测微妙和非线性结构偏差方面的局限性.
提高民用基础设施中异常检测的灵敏度,可靠性和可解释性.

主要方法:

实施了结合CDPF和SEVT的基线方法,用于使用Z24桥梁数据集中的模态频率进行值.
开发了一个集体异常检测框架,集成主要组件分析 (PCA) 和自动编码器 (AE).
PCA捕获线性模式,而AE学习非线性表示,以进行可靠的异常检测.

主要成果:

基线方法成功地发现了渐进性损害场景中的异常.
与基线相比,拟议的整体框架在检测异常方面表现出更好的灵敏度和可靠性.
组合方法显示了对环境和操作变化的增强稳定性.

结论:

基于集体的无监督方法为SHM提供了显著的进展.
综合PCA和AE框架为检测结构异常提供了更强大,更稳定的方法.
这种方法有望实现更有效,更可靠的基础设施健康监测.

关键词:

这就是SHM SHM.Z24 数据集 Z24 数据集适应性权衡是适应性的权衡.检测异常检测异常检测自动编码器自动编码器合并合奏合并合奏合并合奏主要组件分析的主要组件分析没有监督的学习学习.

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