Performance of Camera-Based Vibration Monitoring Systems in Input-Output Modal Identification Using Shaker Excitation

Kalybek, Maksat; Bocian, Mateusz; Pakos, Wojciech; Grosel, Jacek; Nikitas, Nikolaos

doi:10.3390/rs13173471

Cited by 15 publications

(4 citation statements)

References 61 publications

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“…The LK algorithm [11] is the most popular algorithm used for optical flow estimation, but it is limited to tracking targets that have large motion between two consecutive frames. The most prevalent keypoints are extracted by the Harris corner detector [9,13], Shi-Tomasi corner detector [10,14], scale-invariant feature transform (SIFT) algorithm [13], and speeded up robust features (SURF) algorithm [15,16]. However, not all sparse-optical-flow-based target tracking methods used for structural vibration monitoring implement outlier removal methods.…”

Section: Sparse Optical Flowmentioning

confidence: 99%

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Nie

Bodda

Sandhu

et al. 2022

Sensors

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Computer-vision-based target tracking is a technology applied to a wide range of research areas, including structural vibration monitoring. However, current target tracking methods suffer from noise in digital image processing. In this paper, a new target tracking method based on the sparse optical flow technique is introduced for improving the accuracy in tracking the target, especially when the target has a large displacement. The proposed method utilizes the Oriented FAST and Rotated BRIEF (ORB) technique which is based on FAST (Features from Accelerated Segment Test), a feature detector, and BRIEF (Binary Robust Independent Elementary Features), a binary descriptor. ORB maintains a variety of keypoints and combines the multi-level strategy with an optical flow algorithm to search the keypoints with a large motion vector for tracking. Then, an outlier removal method based on Hamming distance and interquartile range (IQR) score is introduced to minimize the error. The proposed target tracking method is verified through a lab experiment—a three-story shear building structure subjected to various harmonic excitations. It is compared with existing sparse-optical-flow-based target tracking methods and target tracking methods based on three other types of techniques, i.e., feature matching, dense optical flow, and template matching. The results show that the performance of target tracking is greatly improved through the use of a multi-level strategy and the proposed outlier removal method. The proposed sparse-optical-flow-based target tracking method achieves the best accuracy compared to other existing target tracking methods.

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Section: Sparse Optical Flowmentioning

confidence: 99%

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Nie

Bodda

Sandhu

et al. 2022

Sensors

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show abstract

“…Recent advancements in sensor technologies such as contact sensors 1 (e.g., fiber optic sensors, inclinometers, 1 School of Civil Engineering, Tianjin University, Tianjin, People's Republic of China and strain gauges), wireless sensors 2 (e.g., accelerometers, acoustic sensors, smart materials, and microelectro-mechanical systems), and vision-based remote monitoring systems 3 (e.g., high-resolution cameras, action cameras, smartphones, consumer-grade cameras, unmanned autonomous vehicles, drones, and sonars) have facilitated the measurement and assessment of structures under various conditions. 4,5 In the last few years, machine learning (ML) algorithms have been mainly designed and applied in vibration-based damage detection tasks to directly extract the damagesensitive features from measured or simulated vibration data based on sensors and carry out pattern recognition of the structure.…”

Section: Introductionmentioning

confidence: 99%

Classification and regression-based convolutional neural network and long short-term memory configuration for bridge damage identification using long-term monitoring vibration data

Yessoufou

Zhu

2023

Structural Health Monitoring

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Considerable attention has recently been focused on classification and regression-based convolutional neural network (CNN) and long short-term memory (LSTM) due to their excellent performance in capturing complex spatial and temporal information characteristics for structural damage identification. However, few studies have considered structural damage identification as a classification and regression problem. In addition, bridges in practical engineering are vulnerable to various environmental and vehicle loading conditions. Hence, this study proposed a new two-stage CNN–LSTM configuration for bridge damage identification using vibration data considering the influence of temperatures. First, a classification-based CNN–LSTM is designed to perform multiclass damage detection tasks, and then a regression-based CNN–LSTM is developed for damage localization and severity prediction tasks. The performance of the proposed damage identification method was evaluated through a simulation dataset of a concrete highway bridge model and a field experiment dataset of Z24-bridge (Switzerland). In addition, a set of statistical evaluation metrics such as sparse categorical cross-entropy loss, accuracy, confusion matrix, mean squared loss, mean absolute error, mean absolute percentage error, and coefficient of determination were used to compare the damage identification performance of the proposed CNN–LSTM configuration with a regular CNN model and conventional machine learning (ML) algorithms. Prediction results indicate that the proposed CNN–LSTM model outperforms the regular CNN model and conventional ML algorithms for bridge damage identification.

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“…However, the physically attached sensors may cause a mass-loading effect on lightweight targets, and they are difficult to affix to complex large-scale structures [ 2 , 3 , 4 ]. As an alternative, the vision-based method is one of the most popular non-contact measurement methods for the structural modal analysis in recent years [ 5 , 6 , 7 ]. Compared with common contact sensors, camera-based devices are more flexible and provide a higher spatial-resolution sensing capacity, which makes them convenient for remote installation and preferable for full-field measurements [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Structural Modal Identification Using Hybrid Motion Magnification

Zhang

Zhu

Hou

et al. 2022

Sensors

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As a promising alternative to conventional contact sensors, vision-based technologies for a structural dynamic response measurement and health monitoring have attracted much attention from the research community. Among these technologies, Eulerian video magnification has a unique capability of analyzing modal responses and visualizing modal shapes. To reduce the noise interference and improve the quality and stability of the modal shape visualization, this study proposes a hybrid motion magnification framework that combines linear and phase-based motion processing. Based on the assumption that temporal variations can represent spatial motions, the linear motion processing extracts and manipulates the temporal intensity variations related to modal responses through matrix decomposition and underdetermined blind source separation (BSS) techniques. Meanwhile, the theory of Fourier transform profilometry (FTP) is utilized to reduce spatial high-frequency noise. As all spatial motions in a video are linearly controllable, the subsequent phase-based motion processing highlights the motions and visualizes the modal shapes with a higher quality. The proposed method is validated by two laboratory experiments and a field test on a large-scale truss bridge. The quantitative evaluation results with high-speed cameras demonstrate that the hybrid method performs better than the single-step phase-based motion magnification method in visualizing sound-induced subtle motions. In the field test, the vibration characteristics of the truss bridge when a train is driving across the bridge are studied with a commercial camera over 400 m away from the bridge. Moreover, four full-field modal shapes of the bridge are successfully observed.

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Performance of Camera-Based Vibration Monitoring Systems in Input-Output Modal Identification Using Shaker Excitation

Cited by 15 publications

References 61 publications

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Computer-Vision-Based Vibration Tracking Using a Digital Camera: A Sparse-Optical-Flow-Based Target Tracking Method

Classification and regression-based convolutional neural network and long short-term memory configuration for bridge damage identification using long-term monitoring vibration data

Vision-Based Structural Modal Identification Using Hybrid Motion Magnification

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