Zhibin Lin

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...