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2023 | OriginalPaper | Buchkapitel

5. Template Matching and Particle Filtering for Structural Identification of High- and Low-Frequency Vibration

verfasst von : Nicholas A. Valente, Celso T. do Cabo, Zhu Mao, Christopher Niezrecki

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

Verlag: Springer International Publishing

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Abstract

Digital image correlation (DIC) has been widely accepted in the vibration community for extracting strain and displacement using noncontact optical techniques. Due to the nature of DIC, the preparation of a test structure with an applied pattern is important for obtaining accurate results. Investigation into patternless optical methods would be beneficial, and it would be ideal if a test structure no longer needed pretreatment prior to optical testing. Recently in the literature, phase-based motion magnification (PMM) has been utilized to exaggerate subtle motion for structural identification. In this work, template matching is used to correlate a template facet over a series of magnified images. Following the determination of a template facet, virtual red, green, and blue (RGB) targets are placed along the principal direction of displacement. Particles are then randomly generated and used to find the RGB-coded targets and clustered to obtain sub-pixel displacements that can be used for frequency extraction of magnified data. Application of the template match particle filter (TMPF) approach will further enhance noncontact sensing, in addition to providing a more efficient way of processing optical data. This method is implemented to experimentally characterize parameters of two structures (i.e., a cantilever beam and bridge) having both high and low frequencies.

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Titel: Template Matching and Particle Filtering for Structural Identification of High- and Low-Frequency Vibration
verfasst von: Nicholas A. Valente
Celso T. do Cabo
Zhu Mao
Christopher Niezrecki
Verlag: Springer International Publishing
Buch: Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6
Print ISBN: 978-3-031-04097-9

Electronic ISBN: 978-3-031-04098-6

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-04098-6_5

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