Data-driven methods for vibration-based monitoring based on singular spectrum analysis

Trendafilova, Irina and Garcia Cava, David and Al-Bugharbee, Hussein; Nobari, Ali Salehzadeh and Aliabadi, M. H. Ferri, eds. (2018) Data-driven methods for vibration-based monitoring based on singular spectrum analysis. In: Vibration-Based Techniques for Damage Detection and Localization in Engineering Structures. Computational and Experimental Methods in Structures . World Scientific, London, pp. 41-73. ISBN 978-1-78634-496-0 (https://doi.org/10.1142/9781786344977_0002)

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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.

ORCID iDs

Trendafilova, Irina ORCID logoORCID: https://orcid.org/0000-0003-1121-7718, Garcia Cava, David ORCID logoORCID: https://orcid.org/0000-0002-3841-6824 and Al-Bugharbee, Hussein; Nobari, Ali Salehzadeh and Aliabadi, M. H. Ferri
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