Vision and Vibration Data Fusion-Based Structural Dynamic Displacement Measurement with Test Validation

程玉秀,; Weng, Yufeng; Shi, Weixing

doi:10.3390/s23094547

Cited by 7 publications

(2 citation statements)

References 56 publications

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“…Billie [13] detected and monitored structural defects in infrastructure to explore intelligent recognition through dynamic and static measurements in displacement monitoring. Xiu [14] proposed an estimation method of structural displacement response based on the fusion concept of contact acceleration monitoring and non-contact displacement identification and obtained structural vibration response and modal information. Wang [15] proposed a new multiple-camera machine vision bridge modal detection method and achieved the extraction of the structural displacement and modal of a bridge through indoor and outdoor experiments.…”

Section: Introductionmentioning

confidence: 99%

Research on Full-Field Dynamic Deflection Measurement of Beams Based on Dense Feature Matching and Mismatch Removal Method

Zheng,

Tang,

Liu

et al. 2024

Applied Sciences

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To solve the problems of measurement errors led by mismatches of dense feature matching in machine vision structural deflection measurement, this paper proposes a dense feature extraction, matching, and dual-step mismatch-removal-based full-field structural dynamic deflection measurement method. First, the of dense feature detection and matching theory is introduced to extract the SIFT feature points on a structural surface in an image sequence and matched by FLANN to trace the structure movement, and the mechanisms and causes of mismatches are analyzed. Then, a dual-step mismatch removal method combining RANSAC and Structural Displacement Continuity Restriction (SDCR) is introduced to achieve full-field dynamic beam deflection measurement. The proposed method is validated through indoor cantilever beam experiments, and results show that the method can effectively eliminate a large number of SIFT feature mismatches (accounting for approximately 55% of the total matched feature points). The full-field dynamic displacement field of the beam can be measured with the correctly matched dense feature points by converting dense feature point displacements into continuous and uniform spatiotemporal deflections of the structure. A comparison with the GOM Correlate Professional DIC measurement system was conducted, and the maximum measurement error of the cantilever beam dynamic displacement of the proposed method is between 0.024 and 0.053 mm, the root mean squared error of displacement is approximately 0.01 mm, and the correlation coefficient between two deflection–time curves reaches 0.9964. The proposed algorithm is proven to be effective in full-field displacement measurement and has great potential in future structural health monitoring of bridges.

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Section: Introductionmentioning

confidence: 99%

Research on Full-Field Dynamic Deflection Measurement of Beams Based on Dense Feature Matching and Mismatch Removal Method

Zheng,

Tang,

Liu

et al. 2024

Applied Sciences

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“…First and foremost, it provides a practical and nonintrusive solution for a vibration monitoring system, which is crucial for ensuring the stability and reliability of the cutting process [26]. Additionally, this approach enables real-time vibration monitoring, providing valuable feedback that can be utilized to adjust cutting parameters and improve cutting quality [27].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Machine Learning of Vibration Amplitude and Surface Roughness after Waterjet Cutting

Leleń,

Biruk-Urban,

Józwik

et al. 2023

Materials

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This study focused on analyzing vibrations during waterjet cutting with variable technological parameters (speed, vfi; and pressure, pi), using a three-axis accelerometer from SEQUOIA for three different materials: aluminum alloy, titanium alloy, and steel. Difficult-to-machine materials often require specialized tools and machinery for machining; however, waterjet cutting offers an alternative. Vibrations during this process can affect the quality of cutting edges and surfaces. Surface roughness was measured by contact methods after waterjet cutting. A machine learning (ML) model was developed using the obtained maximum acceleration values and surface roughness parameters (Ra, Rz, and RSm). In this study, five different models were adopted. Due to the characteristics of the data, five regression methods were selected: Random Forest Regressor, Linear Regression, Gradient Boosting Regressor, LGBM Regressor, and XGBRF Regressor. The maximum vibration amplitude reached the lowest acceleration value for aluminum alloy (not exceeding 5 m/s2), indicating its susceptibility to cutting while maintaining a high surface quality. However, significantly higher acceleration amplitudes (up to 60 m/s2) were registered for steel and titanium alloy in all process zones. The predicted roughness parameters were determined from the developed models using second-degree regression equations. The prediction of vibration parameters and surface quality estimators after waterjet cutting can be a useful tool that for allows for the selection of the optimal abrasive waterjet machining (AWJM) technological parameters.

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Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation

Wang,

Tang,

Dou

et al. 2023

J Nondestruct Eval

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Vision and Vibration Data Fusion-Based Structural Dynamic Displacement Measurement with Test Validation

Cited by 7 publications

References 56 publications

Research on Full-Field Dynamic Deflection Measurement of Beams Based on Dense Feature Matching and Mismatch Removal Method

Research on Full-Field Dynamic Deflection Measurement of Beams Based on Dense Feature Matching and Mismatch Removal Method

Modeling and Machine Learning of Vibration Amplitude and Surface Roughness after Waterjet Cutting

Enhancement of Track Damage Identification by Data Fusion of Vibration-Based Image Representation

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