Enhancing time series data classification for structural damage detection through out-of-distribution representation learning

Abstract

The analysis and classification of time series data, notably within Structural Health Monitoring (SHM) systems, face profound challenges due to the nonstationary nature of the data and changes in distribution over time. Additionally, the complexity of the SHM systems' time series datasets also relates to the presence of elusive latent distributions, making it challenging to pinpoint the dynamics of data distribution shifts. Therefore, this paper uses a Deep Learning (DL) model to address dynamic distribution changes and target elusive latent distributions in the data of SHM systems by adopting an out-of-distribution (OOD) representation learning method. The core idea is that by using adversarial training and domain-invariant feature learning, the used model enhances the analysis of time series data by dividing the data into distinct latent sub-domains, aiming to maximize the variance in distribution between each segment, facilitating a more unified analysis across different data segments. The effectiveness is demonstrated through testing on extensive datasets, including both real-world bridge data and laboratory data, showing a significant improvement over traditional DL models, especially in its ability to generalize to new, unseen distributions.