Modal Property-Based Data Anomaly Detection Method for Autonomous Stay-Cable Monitoring System in Cable-Stayed Bridges

Abstract

This study presents a novel framework for data anomaly detection in stay-cables, aimed at establishing an autonomous monitoring system in cable-stayed bridges. Based on the fact that peaks in the power spectra of cable accelerations appear periodically at constant intervals, we classified the anomalous data into two categories in terms of the data quality and behavioral aspects. The framework provides two thresholds derived from the modal property of stay-cables to identify each anomaly type. To validate the performance of the proposed method, we collected long-term monitoring data from stay-cables in a cable-stayed bridge currently in operation in South Korea. Then, the peak information was extracted by adopting an automatic peak-picking technique. We applied the proposed method to establish thresholds that determine the presence of anomalous data. This study validated that the proposed method can determine anomalous types when new data are used as input.