Performance of signal processing techniques for anomaly detection using a temperature-based measurement interpretation a
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ORIGINAL PAPER
Performance of signal processing techniques for anomaly detection using a temperature‑based measurement interpretation approach Rolands Kromanis1 · Prakash Kripakaran2 Received: 2 May 2020 / Revised: 8 August 2020 / Accepted: 28 August 2020 © The Author(s) 2020
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. Keywords Structural health monitoring (SHM) · Signal analysis · Signal processing · Damage detection · Long term monitoring · Thermal effects
1 Introduction Effective data interpretation approaches [1, 2] are key to support decision-making based on long-term bridge monitoring systems [3–5]. Such systems typically collect dynamic [6, 7] or quasi-static response measurements [8, 9], alongside environmental data such as temperature, humidity and wind. The collected response measurements, while including the effects of live loads and weather-related loads such as wind, are mostly dominated by the effects of daily and seasonal variations in ambient temperature [7–11]. Bridges have also been observed to have non-linear temperature gradients that often cause thermal stresses comparable to those due to live loads [12]. Therefore, reliable techniques for interpreting measurements must include appropriate ways of incorporating temperature effects. The techniques used for measurement interpretation are usually referred to as structural identification * Rolands Kromanis [email protected] 1
Faculty of Engineering Technology, University of Twente, Enschede, The Netherlands
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
2
(St-Id) techniques due to their use of system identification approaches [13, 14]. St-Id aims to develop numerical models that are capable of accurately predicting structural behaviour using measurements from structural health monitoring (SHM) [13]. Historically, in the context of aerospace and mechanical systems, St-Id techniques have been applied primarily for damage identification. Conceptually, damage identification can be considered to be part of a broad measurement interpretation paradigm that has the following five steps, wher
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