Damage Detection for Highly Flexible Beam-Like Structures Using Attenuation Response from Vision-Based Vibration Measurement

39 Pages Posted: 14 Mar 2025

See all articles by Ning GUO

Ning GUO

affiliation not provided to SSRN

Shixiong Wang

affiliation not provided to SSRN

Junhao Lv

affiliation not provided to SSRN

Shancheng Cao

affiliation not provided to SSRN

Chao Xu

affiliation not provided to SSRN

Abstract

Flexible beam-like structures are extensively used in aerospace applications, and their effective damage detection is critical for ensuring structural safety and reliability. This paper introduces a novel damage detection approach for flexible beam-like structures that combines vision-based vibration response testing with the inner product vector of vibration attenuation response. Simulation results indicate that the proposed method outperforms traditional modal curvature-based techniques, offering enhanced accuracy and superior noise resistance. Experimental validation demonstrates the effectiveness of the field-of-view (FOV) parameter estimation technique in improving vibration measurement precision and in accurately locating structural damage. The findings emphasize the robustness and broad applicability of the proposed approach, presenting substantial potential for advancing structural health monitoring of flexible beam-like components in aerospace engineering.

Keywords: beam-like structure, Damage detection, attenuation response, inner product vector, vision-based vibration measurement

Suggested Citation

GUO, Ning and Wang, Shixiong and Lv, Junhao and Cao, Shancheng and Xu, Chao, Damage Detection for Highly Flexible Beam-Like Structures Using Attenuation Response from Vision-Based Vibration Measurement. Available at SSRN: https://ssrn.com/abstract=5179258 or http://dx.doi.org/10.2139/ssrn.5179258

Ning GUO (Contact Author)

affiliation not provided to SSRN ( email )

Shixiong Wang

affiliation not provided to SSRN ( email )

Junhao Lv

affiliation not provided to SSRN ( email )

Shancheng Cao

affiliation not provided to SSRN ( email )

Chao Xu

affiliation not provided to SSRN ( email )

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