【摘 要】
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As an important technique for managing and maintaining civil infrastructure,structural health monitoring(SHM)has been applied worldwide,which produces huge amounts of data.A prerequisite task for all
【机 构】
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Harbin Institute of Technology Haerbin,Heilongjiang,China
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As an important technique for managing and maintaining civil infrastructure,structural health monitoring(SHM)has been applied worldwide,which produces huge amounts of data.A prerequisite task for all SHM systems is data cleansing due to systems malfunctions that are mostly induced by harsh operation conditions.A challenge within is to handle the “treasure or trash” of anomalous SHM data,which means that these data could be generated either by emergencies,such as earthquake and ship collision,or just by system malfunctions.Traditional anomaly detection methods define anomalies only by simple statistical properties,i.e.,the mean,variance,rms,etc.,which are abrupt for complex variation of SHM data.Inspired by the real-world manual inspection,we propose a novel anomaly detection method based on artificial intelligence techniques.
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