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首页> 外文期刊>Journal of structural engineering >Early Damage Detection Based on Pattern Recognition and Data Fusion
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Early Damage Detection Based on Pattern Recognition and Data Fusion

机译：基于模式识别和数据融合的早期损伤检测

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Structural health monitoring (SHM) relies on data acquired from sensorial systems installed on site, and is nowadays being used more often not only for asset management, but also in critical structures when there is the need to detect damage in an early stage, before it impairs structural performance and safety. Early detection of damage in critical structures relies on the acquisition of continuous streams of information and on reliable techniques capable of analyzing it in real time, without generating false alerts. In this context, the combination of data fusion strategies, capable of converting large amounts of data into small pieces of information, with pattern recognition algorithms, which are able to analyze this information in real time, is addressed in the present paper with the objective of developing an original strategy capable of (1)removing the effects of regular actions imposed to structures without the need to measure them and of (2)compressing entire SHM data sets of arbitrary dimensions into a sensitive single-valued damage index. These capabilities are achieved by combining principal component analysis, the broke-stick rule, clustering methods, symbolic data objects, and symbolic distances. The proposed strategy was tested and validated with a numerical model of a cable-stayed bridge, using experimental data as input. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect damage as small as 1% of stiffness reduction in a single stay cable. This sensitivity evidenced by the proposed strategy can be considered particularly high because it was obtained from a small amount of inexpensive sensors with a static character and because it was associated with a false detection incidence of only 0.1%. (C) 2016 American Society of Civil Engineers.
机译：结构健康监测（SHM）依赖于从现场安装的感官系统获取的数据，如今，不仅需要用于资产管理，而且还需要在早期阶段检测出损坏的关键结构中，如今越来越多地使用它。损害结构性能和安全性。对关键结构中的损坏进行早期检测取决于获取连续的信息流以及能够实时分析信息而不产生错误警报的可靠技术。在这种情况下，本文旨在解决将数据融合策略与模式识别算法相结合的问题，该策略可以将大量数据转换成小块信息，并且能够实时分析这些信息，目的是：开发一种原始策略，该策略能够（1）消除对结构施加的常规操作的影响而无需对其进行测量，以及（2）将任意尺寸的整个SHM数据集压缩为敏感的单值损伤指数。通过结合主成分分析，折断规则，聚类方法，符号数据对象和符号距离来实现这些功能。使用实验数据作为输入，通过斜拉桥数值模型对提出的策略进行了测试和验证。从该分析可以看出，在现场测量的噪声水平下，所提出的策略能够自动检测到单根拉索中刚度降低1％的损坏。由提议的策略证明的这种灵敏度可以被认为是特别高的，因为它是从少量具有静态特征的廉价传感器中获得的，并且与仅0.1％的错误检测率相关。 （C）2016年美国土木工程师学会。

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