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Open access Aug 2026

A data separability-driven framework for sensor measurement anomaly classification in structural health monitoring

Reliable sensor data are fundamental to the effectiveness of long-term structural health monitoring (SHM) systems, in which measurement anomalies can compromise condition assessment, damage detection, and maintenance decision-making. This study presents a data separability-driven framework for automated anomaly classif...

Hong Pan, M. Khan, Zhi-Bin Lin · 0 citations

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