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.
Highlights
Effective data interpretation approaches [1, 2] are key to support decision-making based on long-term bridge monitoring systems [3,4,5]
While the basic concept in the temperature-based measurement interpretation (TB-MI) approach was illustrated in [25], there has not been an in-depth investigation into the performance of various signal processing methods within this concept. This is the specific novelty of this paper, which focuses on the performance of four anomaly detection techniques within the TB-MI approach: (1) cointegration, (2) signal subtraction method, (3) moving principal component analysis (MPCA) and (4) moving fast Fourier transform are chosen
The other two techniques—moving principal component analysis and cointegration, have been chosen out of several investigated in previous research due to the superior performance they have demonstrated for damage detection from longterm quasi-static response time histories [22, 27]
Summary
Effective data interpretation approaches [1, 2] are key to support decision-making based on long-term bridge monitoring systems [3,4,5]. A key reason cited for the lack of sensitivity of MPCA was the effects of ambient temperature variations on structural response This was verified by [24] who showed on a laboratory structure that anomaly detectability improves significantly when thermal effects have been purged from measurements. This is the specific novelty of this paper, which focuses on the performance of four anomaly detection techniques within the TB-MI approach: (1) cointegration, (2) signal subtraction method, (3) MPCA and (4) moving fast Fourier transform are chosen These techniques have been chosen for the superior performance they have demonstrated in previous studies [22, 23, 25, 27] on anomaly detection from long-term measurements. The performance of the TB-MI approach is compared with direct application of anomaly detection techniques on response measurements
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