Identification of Vibration Frequencies of Railway Bridges from Train-Mounted Sensors Using Wavelet Transformation

Erduran, Emrah; Pettersen, Fredrik Marøy; Gönen, Semih; Lau, Albert

doi:10.3390/s23031191

Cited by 11 publications

(9 citation statements)

References 58 publications

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“…The wavelet transform inherits and develops the idea of localisation of the Fourier transform, while overcoming the disadvantages of the window size not varying with frequency. Its main feature is to highlight the characteristics of certain aspects of the signal after the transform, and to gradually refine the signal on multiple scales through the telescopic translation operation, eventually achieving time subdivision at high frequencies and frequency subdivision at low frequencies, which can automatically adapt to the requirements of time-frequency signal analysis and thus focus on arbitrary details of the signal [19,20,21]. Therefore, wavelet transform has been successfully applied to many fields, especially the discrete numerical algorithm of wavelet transform is widely used in the study of many theoretical problems.…”

Section: Wavelet Transform Modulus Maximamentioning

confidence: 99%

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

You¹,

Han²,

Liu³

et al. 2023

Preprint

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In the course of routine bridge maintenance, edge detection allows the partial condition of the bridge to be viewed. However, many edge detection methods often have unsatisfactory performances when dealing with images with complex backgrounds. Moreover, the processing often involves the manual selection of thresholds, which can result in repeated testing and comparisons. To addresses these problems in this paper, the wavelet transform modulus maxima method is used to detect the target image, and then the threshold value of the image can be determined automatically according to the OTSU method to remove the pseudo-edges. Thus, the real image edges can be detected. The results show that the information entropy and SSIM of the detection results are the highest when compared with the commonly used Canny and Laplace algorithms, which means that the detection quality is optimal. To more fully illustrate the advantages of the algorithms, images with more complex backgrounds was detected, and the processing results of the algorithms in this paper are still optimal. In addition, the automatic selection of thresholds saves the operator's effort and improves the detection efficiency. Thanks to the combined use of the above two methods, detection quality and efficiency are significantly improved, which has a good application in engineering practice.

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Section: Wavelet Transform Modulus Maximamentioning

confidence: 99%

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

You¹,

Han²,

Liu³

et al. 2023

Preprint

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“…Drive-by monitoring systems offer advantages over vision-based damage identification systems, as signals collected from the vehicle can reveal structural changes caused by damage on a bridge, providing valuable insights into the impact of the damage on the overall behavior of the structure [13]. As a result, this approach has gained popularity and undergone further development to extract the dynamic characteristics of bridges including natural frequencies [14][15][16][17][18][19], mode shapes [20][21][22][23][24], and damping ratios [25][26][27]. Some studies focused on estimating and characterizing road profiles [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Drive-by Bridge Damage Detection Using Continuous Wavelet Transform

Demirlioglu,

Erduran

2024

Applied Sciences

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Bridges serve as vital engineering structures crafted to facilitate secure and effective transportation networks. Throughout their life-cycle, they withstand various factors, including diverse environmental conditions, natural hazards, and substantial loads. Recent bridge failures underscore the significant risks posed to the structural integrity of bridges. Damage detection techniques, being core components of structural health monitoring, play a crucial role in objectively assessing bridge conditions. This article introduces a novel framework for identifying damage in bridges utilizing continuous wavelet analysis of accelerations recorded using two sensors mounted on a vehicle traversing the bridge. The proposed method leverages changes in the static response of the bridge, which has proven to be more sensitive to damage than its dynamic counterpart. By doing so, the method eliminates the reliance on modal parameters for damage identification, addressing a significant challenge in the field. The proposed framework also addresses key challenges encountered by drive-by monitoring methods. It mitigates the adverse effects of road roughness by utilizing residual accelerations and efficiently detects and locates damage even in the absence of corresponding data from an undamaged bridge. Numerical investigations demonstrate the robustness of the proposed method against various parameters, including damage location and extent, vehicle speeds, road roughness levels, different boundary conditions, and multi-damage scenarios.

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“…VSM promises efficient and low-cost inspection for the condition assessment of bridges without the requirement of instrumentation of each bridge. Thus, VSM has been further developed to extract a wide range parameters such as natural frequencies [ 13 , 14 , 15 ], mode shapes [ 16 , 17 ], damping ratios [ 18 , 19 ], and damage occurring on the bridge [ 20 , 21 , 22 , 23 ]. Among these parameters, mode shapes are particularly important because it serves as a key indicator for creating and updating finite element (FE) models of bridges as well as detecting local and global damage.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports

Demirlioglu

Gönen

Erduran

2023

Sensors

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This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its travel. Two of the evaluated methods use a signal decomposition technique to extract the modal components of the bridge from the contact point of the response while the third one relies on the segmentation of the measured signals along the bridge deck and applying an operational modal analysis tool to each segmented signal to estimate the mode shapes. Numerical analyses are conducted on one single- and one two-span bridge, considering smooth and rough road profiles, different vehicle speeds, and presence of lead vehicle. The accuracy of the numerical models used in developing and assessing vehicle scanning models is tested, and the results of the study demonstrate the method using a half-car vehicle model and signal decomposition technique shows robustness against increasing vehicle speeds and road roughness while the method applying the segmentation of the measured signals provides relatively accurate mode shape estimates at the bridge edges at low speed, although the three methods have their limitations. It is also observed that simplified bridge and vehicle models can hide potential challenges that arise from the complexity of actual vehicle and bridge systems. Considering that a significant number of bridges worldwide are built on elastic supports, the practical success of vehicle scanning methods depends on their ability to handle elastic boundary conditions with reliability. Therefore, the article provides valuable insights into the capabilities and limitations of the current vehicle scanning methods, paving the way for further advancements and refinements in these techniques.

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Identification of Vibration Frequencies of Railway Bridges from Train-Mounted Sensors Using Wavelet Transformation

Cited by 11 publications

References 58 publications

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

Drive-by Bridge Damage Detection Using Continuous Wavelet Transform

Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports

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