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

Abstract

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 classification of sensor measurements in bridge-based SHM applications. The framework integrates wavelet-based denoising, class balancing, guided data cleaning, multi-representation feature encoding, and deep-learning-based classification. To improve class distinction, 1-h signal segments are transformed into complementary representations, including time-frequency (TF) images, Gramian angular fields, and Markov transition fields. The framework was validated using month-long acceleration measurements collected from a full-scale cable-stayed bridge comprising 28,272 window-level samples from 38 accelerometers across six anomaly classes and one normal class. Quantitative analyses show that class balancing improves classification accuracy from 95.3 to 97.2%, while subsequent label refinement further improves accuracy to 99.2%. Feature fusion provides a further improvement, achieving 99.4% accuracy with Macro-F1 and Weighted-F1 scores exceeding 0.99. The empirical separability score increases from 0.33 for the original dataset to 0.70 for the fully processed dataset, demonstrating a strong relationship between enhanced class distinction and classification performance. The proposed framework provides a practical methodology for automated measurement-data quality assessment in long-term SHM systems. Although the results demonstrate strong performance on the adopted benchmark dataset, further validation using independent monitoring systems and grouped validation strategies is needed to establish cross-sensor and cross-site generalization.