구조 건전성 모니터링 데이터의 결함 검출 기법 고찰 = Data Fault detection of Structural Health Monitoring System(SHMS)

다국어 초록 (Multilingual Abstract)

In recent years, the application of Structural Health Monitoring (SHM) technology has rapidly expanded to enhance the long-term safety and maintenance efficiency of civil infrastructure. The large-scale data collected from sensor networks in such monitoring systems may contain various types of faults—such as sensor malfunction, signal drift, data loss, and noise contamination—which can significantly degrade the reliability of structural condition assessments.
This study comprehensively investigates both data-driven and model-based approaches for the early detection of data faults acquired from Structural Health Monitoring Systems (SHMS). Statistical anomaly detection methods, frequency-domain analyses, machine learning algorithms, and hybrid fault detection techniques were examined to compare their strengths and limitations. The comparative evaluation also assessed the practical applicability of these methods to bridge monitoring systems and digital sensing networks.