Identifying frequency-domain operating deflection shapes and internal damage in structures using geometric vision method

• Depth-parametrized stereo vision enables geometric optical ODS sensing • Consistent calibration enhances stereo rectification for vibration analysis • Linear model maps image motion to frequency-domain ODS responses • Wavelet-based algorithm detects structural damage from ODS fields • Findings support automated vibration and damage monitoring in service Operating deflection shape (ODS) analysis characterizes structural vibration in the frequency domain without requiring prior knowledge of excitation, offering an automatic means to assess dynamic behavior and detect damage in service. Building on this, we present a geometric vision–based sensing method for ODS identification under a depth-parameterization framework, enabling direct recovery of full-field vibration responses from spatiotemporal image motion captured by a stereo imaging system. To ensure accurate ODS reconstruction, a random sample consensus–based calibration algorithm is developed to rectify stereo geometry and establish a depth-parameterized projective system. Within this framework, we formulate a linear model that maps measured image motion of object points to their ODS responses in the frequency domain. In addition, a wavelet-based algorithm is proposed to extract damage parameters from defective structures using the reconstructed ODS fields. Experimental validations confirm that the proposed vision-based sensing method and damage identification approach are both effective and robust, providing a promising pathway for structural vibration monitoring and health assessment in complex operational environments.

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Publication Details

Journal
Results in Engineering
Published
2025-11-30
DOI
https://doi.org/10.1016/j.rineng.2025.108474
Primary Topic
Structural Health Monitoring Techniques
Type
article
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article

Identifying frequency-domain operating deflection shapes and internal damage in structures using geometric vision method

Zhangyu Yao, Lei Lü, J. X. Zuo, Zhilong Su et al.
Results in Engineering
Structural Health Monitoring Techniques
article

Identifying frequency-domain operating deflection shapes and internal damage in structures using geometric vision method

Zhangyu Yao, Lei Lü, J. X. Zuo, Zhilong Su, Haoran Zhang, Deyang Zhang, Wei Jun Pan
article en

Abstract

• Depth-parametrized stereo vision enables geometric optical ODS sensing • Consistent calibration enhances stereo rectification for vibration analysis • Linear model maps image motion to frequency-domain ODS responses • Wavelet-based algorithm detects structural damage from ODS fields • Findings support automated vibration and damage monitoring in service Operating deflection shape (ODS) analysis characterizes structural vibration in the frequency domain without requiring prior knowledge of excitation, offering an automatic means to assess dynamic behavior and detect damage in service. Building on this, we present a geometric vision–based sensing method for ODS identification under a depth-parameterization framework, enabling direct recovery of full-field vibration responses from spatiotemporal image motion captured by a stereo imaging system. To ensure accurate ODS reconstruction, a random sample consensus–based calibration algorithm is developed to rectify stereo geometry and establish a depth-parameterized projective system. Within this framework, we formulate a linear model that maps measured image motion of object points to their ODS responses in the frequency domain. In addition, a wavelet-based algorithm is proposed to extract damage parameters from defective structures using the reconstructed ODS fields. Experimental validations confirm that the proposed vision-based sensing method and damage identification approach are both effective and robust, providing a promising pathway for structural vibration monitoring and health assessment in complex operational environments.

Results in EngineeringVol. 29
Shanghai University (CN), Shanghai University of Engineering Science (CN), Henan University of Technology (CN), Vision Technology (United States) (US), Wuhan Ship Development & Design Institute (CN)
Openalex Percentile: Top 62%
Structural Health Monitoring Techniques
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