Population-Based SHM Under Environmental Variability Using a Classifier for Unsupervised Damage Detection
YACINE BEL-HADJ,
WOUT WEIJTJENS
Abstract:In this paper, we introduce a novel deep learning technique for anomaly detection in the context of Population-Based Structural Health Monitoring (PB-SHM). The proposed method eliminates manual feature engineering by utilizing Power Spectral Density (PSD) as input, allowing examination of the entire spectrum. It is based on an auxiliary classification task, wherein the model is trained to discriminate between different systems according to their dynamic response. The classifier confidence is then used during i… Show more
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