This study investigates the effectiveness of four signal processing techniques in supporting a data-driven strategy for anomaly detection that relies on correlations between measurements of bridge response and temperature distributions. The strategy builds upon the regression-based thermal response prediction methodology which was developed by the authors to accurately predict thermal response from distributed temperature measurements. The four techniques that are investigated as part of the strategy are moving fast Fourier transform, moving principal component analysis, signal subtraction method and cointegration method. The techniques are compared on measurement time histories from a laboratory structure and a footbridge at the National Physical Laboratory. Results demonstrate that anomaly events can be detected successfully depending on the magnitude and duration of the event and the choice of an appropriate anomaly detection technique.

这项研究调查了四种信号处理技术在支持数据驱动的异常检测策略中的有效性，该策略依赖于电桥响应和温度分布的测量之间的相关性。该策略基于作者开发的基于回归的热响应预测方法，可以根据分布式温度测量准确预测热响应。作为该策略的一部分，研究的四种技术是快速傅里叶变换，移动主成分分析，信号减法和协整方法。在国家物理实验室的实验室结构和人行天桥的测量时间历史上对这些技术进行了比较。