Vision-Based Dynamic Response Extraction and Modal Identification of Simple Structures Subject to Ambient Excitation

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 4; p. 962
• Main Authors: Chen, Zhiwei, Ruan, Xuzhi, Zhang, Yao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2023
• Subjects: Accelerometers; Accuracy; Algorithms; ambient excitation; Amplitudes; Analysis; cameras; Cantilever beams; computer science; Dynamic response; Eigenvalues; Environmental testing; Excitation; Fourier transforms; low-amplitude vibration; Modal analysis; Modal identification; mode shape identification; Optics; Parameter identification; Photogrammetry; Remote sensing; Resonant frequencies; Signal processing; Signal reconstruction; Structural health monitoring; Vibration; Vibration monitoring; Vision; vision-based
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15040962

Vision-based modal analysis has gained popularity in the field of structural health monitoring due to significant advancements in optics and computer science. For long term monitoring, the structures are subjected to ambient excitation, so that their vibration amplitudes are quite small. Hence, although natural frequencies can be usually identified from the extracted displacements by vision-based techniques, it is still difficult to evaluate the corresponding mode shapes accurately due to limited resolution. In this study, a novel signal reconstruction algorithm is proposed to reconstruct the dynamic response extracted by the vision-based approach to identify the mode shapes of structures with low amplitude vibration due to environmental excitation. The experimental test of a cantilever beam shows that even if the vibration amplitude is as low as 0.01 mm, the first two mode shapes can be accurately identified if the proposed signal reconstruction algorithm is implemented, while without the proposed algorithm, they can only be identified when the vibration amplitude is at least 0.06 mm. The proposed algorithm can also perform well with various camera settings, indicating great potential to be used for vision-based structural health monitoring.