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首页> 外文期刊>Structural health monitoring >Damage assessment with state-space embedding strategy and singular value decomposition under stochastic excitation
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Damage assessment with state-space embedding strategy and singular value decomposition under stochastic excitation

机译：随机激励下状态空间嵌入策略与奇异值分解的损伤评估

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摘要

A multivariate time-series analysis employing a state-space embedding strategy and singular value decomposition is presented in this article to detect infrastructure damage. After summarizing the current state-space reconstruction method, the univariate state-space reconstruction is extended to multivariate (or global) reconstruction for observed time series at multiple locations. Under the hypothesis that reconstructed phase state geometry will change with damage, a reduced feature based on Mahalanobis distance of the most significant singular value vector, which is calculated from the reconstructed trajectory, is proposed. Both the area under receiver operating characteristic curve and deflection coefficient are used as comparison metrics to illustrate the presence and severity of damage. The advantage of this proposed approach is computational efficiency and easy implementation using state-space methodology since it does not require high-dimensional neighbor searches, as previous methods have proposed. Validation of the approach is demonstrated using a 6-degree-of-freedom linear spring-mass system and the IASC-ASCE 4-story benchmark experimental structure. Results from both test beds show that damage occurrence and severity can be successfully identified.
机译：本文提出了一种采用状态空间嵌入策略和奇异值分解的多元时间序列分析，以检测基础设施的破坏。在总结了当前的状态空间重构方法后，将单变量状态空间重构扩展为用于多个位置的观测时间序列的多元（或全局）重构。在重建相态几何结构会随损伤而变化的假设下，提出了一种基于最大轨迹奇异值矢量的马哈拉诺比斯距离的减少特征，该距离是根据重建轨迹计算得出的。接收器工作特性曲线下的面积和挠度系数均用作比较指标，以说明损坏的存在和严重程度。这种提议的方法的优点是计算效率高，并且易于使用状态空间方法实现，因为它不需要像以前的方法那样需要进行高维邻居搜索。使用6自由度线性弹簧质量系统和IASC-ASCE 4层基准实验结构证明了该方法的有效性。两个测试台的结果表明，可以成功识别损坏的发生和严重程度。

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