@inbook{676d623cafde49cd989f6f0378a46366,
title = "Data-driven methods for vibration-based monitoring based on singular spectrum analysis",
abstract = "This chapter studies the application of data-driven methods and specifically principal component analysis (PCA) and singular spectrum analysis (SSA) for purposes of damage assessment in structures and machinery. In this study, data analysis methods PCA and SSA are applied to the measured vibration signals in order to extract information about the state of the structure/machinery and the presence of a fault in it. Two applications are offered, one for damage assessment on a wind turbine blade and another one for fault diagnosis in rolling element bearings. The results demonstrate strong capabilities of the investigated methodology for both structural damage detection and rolling element fault diagnosis. Eventually, a discussion about the capabilities of the studied methodology and the way forward regarding extending its capabilities and applications is offered.",
keywords = "singular spectrum analysis, principal component analysis, outlier principle, wind turbine blade, structural monitoring",
author = "Irina Trendafilova and {Garcia Cava}, David and Hussein Al-Bugharbee",
year = "2018",
month = jul,
day = "31",
doi = "10.1142/9781786344977_0002",
language = "English",
isbn = "978-1-78634-496-0",
series = "Computational and Experimental Methods in Structures",
publisher = "World Scientific",
pages = "41--73",
editor = "Nobari, {Ali Salehzadeh} and Aliabadi, {M. H. Ferri}",
booktitle = "Vibration-Based Techniques for Damage Detection and Localization in Engineering Structures",
}