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

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 succes...

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Bibliographic Details
Published in:Structural control and health monitoring Vol. 28; no. 5
Main Authors: Silva, Moisés Felipe, Santos, Adam, Santos, Reginaldo, Figueiredo, Eloi, Costa, João C.W.A.
Format: Journal Article
Language:English
Published: Pavia John Wiley & Sons, Inc 01.05.2021
Subjects:
Damage detection
damage identification
damage‐sensitive features
data normalization
Entropy (Information theory)
Environmental effects
Feature extraction
Identification methods
Machine learning
Monitoring
Parameter identification
Performance enhancement
stacked autoencoders
System identification
unsupervised learning
Vibration
ISSN:1545-2255, 1545-2263
Online Access:Get full text
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Summary: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.
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ISSN:1545-2255
1545-2263
DOI:10.1002/stc.2714