Performance of signal processing techniques for anomaly detection using a temperature-based measurement interpretation approach

Abstract

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.