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2019 | OriginalPaper | Buchkapitel

8. Mapping Motion-Magnified Videos to Operating Deflection Shape Vectors Using Particle Filters

verfasst von : Aral Sarrafi, Zhu Mao

Erschienen in: Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6

Verlag: Springer International Publishing

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Abstract

Phase-based motion estimation and magnification are targetless methods that have been used recently to perform experimental modal analysis (EMA) and operational modal analysis (OMA) on a variety of structures. Mapping the motion-magnified sequence of images into quantified operating deflection shape (ODS) vectors is currently being conducted via edge detection methods that require intensive human supervision and interference. Within this study, a new hybrid computer vision approach is introduced to extract the quantified ODS vectors from the motion-magnified sequence of images with minimal human supervision. The particle filter point tracking method is utilized to follow the desired feature points in the motion-magnified sequence of images. Moreover, the k-means clustering algorithm is employed as an unsupervised learning approach to performing the segmentation of the particles and assigning them to specific feature points in the in the motion-magnified sequence of images. This study shows that the cluster centers can be employed to estimate the ODS vectors, and the performance of the proposed methodology is evaluated experimentally on a lab-scale cantilever beam and validated via a finite element model.

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Titel: Mapping Motion-Magnified Videos to Operating Deflection Shape Vectors Using Particle Filters
verfasst von: Aral Sarrafi
Zhu Mao
Verlag: Springer International Publishing
Buch: Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6
Print ISBN: 978-3-030-12934-7

Electronic ISBN: 978-3-030-12935-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-12935-4_8

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