Motion magnification for optical-based structural health monitoring

Applications of motion magnification has been seen as an effective way to extract pertinent structural health monitoring data without the use of instrumentation. In particular, phase-based motion magnification (PMM) has been adopted to amplify subtle motions that cannot be seen clearly without further processing. For large infrastructure, this tool can be helpful in identifying the dynamic range of motion and modal frequencies. The use of accelerometers poses a problem for structures that contain large geometry, due to the complexities that arise when attempting to setup a modal test. Optically, one can identify singular points or regions of interest that capture a large range of motion for a structure. These regions of interest ultimately provide the dynamic information that is needed to perform structural health monitoring (SHM) of a complex system. This paper aims to identify a shift in frequency and operational deflection shapes due to varying loading scenarios while using PMM. The ability to capture multiple points without being limited by a data acquisition system permits further analysis of structural health. For example, the ability to apply varying loading scenarios can provide warnings as to how a frequency shifts while sustaining a particular force. Due to the plethora of loading conditions, the variation in external loading makes SHM a more conclusive process. For instance, it was applied many different scenarios for loading conditions and damages to observe the shifts in the frequencies due to each factor. It was also done testing with different sensing techniques and with traditional sensing to verify the reliability of PMM. The tests were done in laboratory structures and in real structures to prove the applicability of PMM and to verify what information is needed to identify damage in the structure.

[1]  Charles R. Farrar,et al.  Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification , 2017 .

[2]  Janko Slavič,et al.  Frequency domain triangulation for full-field 3D operating-deflection-shape identification , 2019, Mechanical Systems and Signal Processing.

[3]  Christopher Niezrecki,et al.  Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification , 2018, ArXiv.

[4]  Billie F. Spencer,et al.  Vision-Based Modal Survey of Civil Infrastructure Using Unmanned Aerial Vehicles , 2019, Journal of Structural Engineering.

[5]  Janko Slavič,et al.  Experimental modal analysis on full-field DSLR camera footage using spectral optical flow imaging , 2018, Journal of Sound and Vibration.

[6]  Ricardo Zaurin,et al.  Structural Health Monitoring With Emphasis On Computer Vision, Damage Indices, And Statistical Analysis , 2009 .

[7]  Keith Worden,et al.  A machine learning approach to nonlinear modal analysis , 2017 .

[8]  Charles R. Farrar,et al.  Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures , 2018 .

[9]  Chuan-Zhi Dong,et al.  Marker-free monitoring of the grandstand structures and modal identification using computer vision methods , 2018, Structural Health Monitoring.

[10]  Eiichi Sasaki,et al.  Nonlinear features for damage detection on large civil structures due to earthquakes , 2012 .

[11]  Peter Avitabile,et al.  Feasibility of extracting operating shapes using phase-based motion magnification technique and stereo-photogrammetry , 2017 .

[12]  Hao Sun,et al.  Camera-Based Vibration Measurement of the World War I Memorial Bridge in Portsmouth, New Hampshire , 2018, Journal of Structural Engineering.

[13]  Peyman Poozesh,et al.  A Comparison of Computer-Vision-Based Structural Dynamics Characterizations , 2017 .

[14]  Hashim Abdul Razak,et al.  Data mining-based damage identification of a slab-on-girder bridge using inverse analysis , 2020 .