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Damage‐sensitive feature extraction with stacked autoencoders for unsupervised damage detection
Author(s) -
Silva Moisés Felipe,
Santos Adam,
Santos Reginaldo,
Figueiredo Eloi,
Costa João C.W.A.
Publication year - 2021
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.2714
Subject(s) - modal , feature extraction , computer science , pattern recognition (psychology) , artificial intelligence , entropy (arrow of time) , structural health monitoring , data mining , machine learning , engineering , structural engineering , chemistry , physics , quantum mechanics , polymer chemistry
Summary 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.