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2023 | OriginalPaper | Buchkapitel

17. An Optical Temporal and Spatial Vibration-Based Damage Detection Using Convolutional Neural Networks and Long Short-Term Memory

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Abstract

Structural dynamics provide critical information for structural health monitoring (SHM), such as changes in the modal behavior which indicate damages. However, for complex systems with noisy operational environments, many factors may influence the estimation of natural frequencies and other modal domain SHM features, such as varying mass distribution of bridges and the variation due to fluctuating temperature and unideal boundary conditions. For this reason, allying the mode shapes with the natural frequencies to forecast damages would pose a more robust solution. Among the techniques existent to perform damage detection, data-driven models, such as machine learning algorithms, are becoming widely used currently. For mode shape extraction, convolutional neural networks (CNN) have been applied to imagery data, allowing to extract full-field mode shapes with a denser spatial resolution (quasi-full field) of the structure if compared to traditional hardware. Combining CNN with long short-term memory (LSTM) network will associate the temporal dependency of the frames with its features which will be more specific for SHM decision-makings. In addition, for the circumstances with low vibration amplitude and subpixel image resolution, applying phase-based motion estimation (PME) and phase-based motion magnification (PMM) allows to extract the natural frequencies with subtle motion magnified at the resonances aiding to emphasize the dynamic features desired. As the training of the deep learning model, a lab-scale truss structure was adopted with different load conditions in order to obtain the required data, and the performance is cross-validated.

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Literatur
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Zurück zum Zitat Sarrafi, A., Poozesh, P., Mao, Z.: A comparison of computer-vision-based structural dynamics characterizations. In: Model validation and uncertainty quantification, volume 3: proceedings of the 35th IMAC, a conference and exposition on structural dynamics, pp. 295–301 (2017) Sarrafi, A., Poozesh, P., Mao, Z.: A comparison of computer-vision-based structural dynamics characterizations. In: Model validation and uncertainty quantification, volume 3: proceedings of the 35th IMAC, a conference and exposition on structural dynamics, pp. 295–301 (2017)
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Zurück zum Zitat Poozesh, P., Sarrafi, A., Mao, Z., Avitabile, P., Niezrecki, C.: Feasibility of extracting operating shapes using phase-based motion magnification technique and stereo-photogrammetry. J. Sound Vib. 407, 350–366 (2017)CrossRef Poozesh, P., Sarrafi, A., Mao, Z., Avitabile, P., Niezrecki, C.: Feasibility of extracting operating shapes using phase-based motion magnification technique and stereo-photogrammetry. J. Sound Vib. 407, 350–366 (2017)CrossRef
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Zurück zum Zitat Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., Mascareñas, D.: Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification. Mech. Syst. Signal Process. 85, 567–590 (2017)CrossRef Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., Mascareñas, D.: Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification. Mech. Syst. Signal Process. 85, 567–590 (2017)CrossRef
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Zurück zum Zitat Zaurin, R., Catbas, N.: Structural health monitoring with emphasis on computer vision, damage indices, and statistical analysis. Ph.D. dissertation, College of Eng. and Computer Sc., University of Central Florida, Orlando (2009) Zaurin, R., Catbas, N.: Structural health monitoring with emphasis on computer vision, damage indices, and statistical analysis. Ph.D. dissertation, College of Eng. and Computer Sc., University of Central Florida, Orlando (2009)
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Zurück zum Zitat Yang, Y., Dorn, C., Mancini, T., Talken, Z., Theiler, J., Kenyon, G., Farrar, C., Mascareñas, D.: Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures. Struct. Health Monit. 17, 1475921717704385 (2017a). https://doi.org/10.1177/1475921717704385 CrossRef Yang, Y., Dorn, C., Mancini, T., Talken, Z., Theiler, J., Kenyon, G., Farrar, C., Mascareñas, D.: Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures. Struct. Health Monit. 17, 1475921717704385 (2017a). https://​doi.​org/​10.​1177/​1475921717704385​ CrossRef
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Zurück zum Zitat do Cabo, C., Mao, Z.: An optical mode shape-based damage detection using convolutional neural networks. In: Rotating machinery, optical methods & scanning LDV methods, Volume 6, pp. 157–162. Springer, Cham (2022)CrossRef do Cabo, C., Mao, Z.: An optical mode shape-based damage detection using convolutional neural networks. In: Rotating machinery, optical methods & scanning LDV methods, Volume 6, pp. 157–162. Springer, Cham (2022)CrossRef
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Zurück zum Zitat Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.: Phase-based video motion processing. ACM Trans. Gr. 32(4), Article 80 (2013)CrossRef Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.: Phase-based video motion processing. ACM Trans. Gr. 32(4), Article 80 (2013)CrossRef
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Zurück zum Zitat Sarrafi, A., Mao, Z., Niezrecki, C., Peyman, P.: Vibration-based damage detection in wind turbine blades using phase-based motion estimation and motion magnification. J. Sound Vib. 421(12), 300–318 (2018)CrossRef Sarrafi, A., Mao, Z., Niezrecki, C., Peyman, P.: Vibration-based damage detection in wind turbine blades using phase-based motion estimation and motion magnification. J. Sound Vib. 421(12), 300–318 (2018)CrossRef
Metadaten
Titel
An Optical Temporal and Spatial Vibration-Based Damage Detection Using Convolutional Neural Networks and Long Short-Term Memory
verfasst von
Celso T. do Cabo
Zhu Mao
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-031-04098-6_17

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