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Article
Open AccessA strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders
Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness (OOR) dama...
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Chapter
Railway Bridges Health Monitoring Supported by Artificial Intelligence
This chapter discusses the detection of damages in railway bridges based on vibration responses induced by traffic and using bridge health monitoring systems. To achieve this goal, an innovative data-driven Ar...
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Chapter
Condition Monitoring of Rolling Stock Supported by Artificial Intelligence Technique
The increasing use of condition monitoring of the railway infrastructure has led railway companies to take advantage of artificial intelligence (AI) technologies. The main goal of this research is to provide a...
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Chapter
Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses
This chapter addresses unsupervised damage detection in railway bridges by presenting a novel AI-based SHM strategy using traffic-induced dynamic responses. To achieve this goal a hybrid combination of wavelet...
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Article
Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring system
This paper presents a progressive numerical model validation of a bowstring-arch railway bridge based on the analysis of experimental data from different structural response measurements, namely, static deform...
