Damage-sensitive feature extraction with stacked autoencoders for unsupervised damage detection

Abstract

In most real-world monitoring scenarios, the lack of measurements from damaged conditions requires the application of unsupervised approaches, mainly the ones based on modal features estimated from raw vibration data through traditional system identification methods. Although numerous successful applications using modal parameters have been reported, they have demonstrated to be insufficient to estimate a robust set of damage-sensitive features. Inspired by the idea of compressed sensing and deep learning, an intelligent two-level feature extraction approach using stacked autoencoders over pre-processed vibration data is proposed. This procedure can improve the performance of traditional damage detection classifiers by compressing modal parameters into a smaller set of highly informative features when considering information entropy metrics. The proposed technique demonstrates significant improvement in the performance of damage detection and classification approaches when evaluated on the well-known monitoring data sets from the Z-24 Bridge, where several damage scenarios were carried out under rigorous operational and environmental effects.