A variance-based approach for the detection and localization of cracks in a beam

Kumar, Ramnivas; Singh, Sachin

doi:10.1016/j.istruc.2022.08.068

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…Integration of optical flow and information entropy was used by Xin et al [21] and similar advanced algorithms have been reported. Similar high-end algorithms have been reported for the prognosis of stationary structures like structural beams by Al-hababi et al [22], and Kumar and Singh [23].…”

Section: Introductionsupporting

confidence: 76%

An algorithm for localization of fatigue crack in spinning rotor based on proof by negation

Teyi,

Singh

2023

Eng. Res. Express

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This paper presents an innovative signal-based approach for detection and localization of a fatigue crack induced in spinning rotors. For development of the algorithm and demonstration of the its capabilities, a conventional rotor supported by rigid end bearings has been considered. In this demonstration, a cracked rotor is simulated using finite elements with four degrees of freedom per node. The model accounts for the gyroscopic effects caused by the offset disc and the breathing of the fatigue crack. The gyroscopic effects are accounted for by the introduction of the gyroscopic matrix in the finite element formulation, and the crack breathing effect is considered by introduction of the crack excitation function in the equations of motion The developed algorithm can also be used to simultaneously determine the magnitude and direction of the disc unbalance relative to the crack front. Also, the algorithm is used to validate the crack location hypothesis at a single node by introducing a variable crack location flag vector. The crack location flag vector with the highest crack stiffness value accurately represents the true crack location.

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Section: Introductionsupporting

confidence: 76%

An algorithm for localization of fatigue crack in spinning rotor based on proof by negation

Teyi,

Singh

2023

Eng. Res. Express

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“…The current work focuses on vibration-based methods that rely on the fact that structural damage will affect the dynamic characteristics of a structure. Different approaches that belong in this category have been applied in the past [ 2 , 3 , 4 , 5 , 6 ], and methods that can be used in real-time scenarios have also been presented [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function

Zacharakis,

Giagopoulos

2024

Sensors

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A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity.

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“…At the same time, some famous scholars have used advanced mathematical methods to locate and detect cracks. Ramnivas Kumar et al proposed a variance-based crack detection and localization method in beams [ 23 ]. Sara Nasiri et al used data-driven technology to predict the fatigue of metals, composites, and 3D-printed parts [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques

Yang

Lin

et al. 2022

Sensors

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The image of expressway asphalt pavement crack disease obtained by a three-dimensional line scan laser is easily affected by external factors such as uneven illumination distribution, environmental noise, occlusion shadow, and foreign bodies on the pavement. To locate and extract cracks accurately and efficiently, this article proposes a three-stage asphalt pavement crack location and segmentation method based on traditional digital image processing technology and deep learning methods. In the first stage of this method, the guided filtering and Retinex methods are used to preprocess the asphalt pavement crack image. The processed image removes redundant noise information and improves the brightness. At the information entropy level, it is 63% higher than the unpreprocessed image. In the second stage, the newly proposed YOLO-SAMT target detection model is used to locate the crack diseases in asphalt pavement. The model is 5.42 percentage points higher than the original YOLOv7 model on mAP@0.5, which enhances the recognition and location ability of crack diseases and reduces the calculation amount for the extraction of crack contour in the next stage. In the third stage, the improved k-means clustering algorithm is used to extract cracks. Compared with the traditional k-means clustering algorithm, this method improves the accuracy by 7.34 percentage points, the true rate by 6.57 percentage points, and the false positive rate by 18.32 percentage points to better extract the crack contour. To sum up, the method proposed in this article improves the quality of the pavement disease image, enhances the ability to identify and locate cracks, reduces the amount of calculation, improves the accuracy of crack contour extraction, and provides a new solution for highway crack inspection.

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A variance-based approach for the detection and localization of cracks in a beam

Cited by 9 publications

References 30 publications

An algorithm for localization of fatigue crack in spinning rotor based on proof by negation

An algorithm for localization of fatigue crack in spinning rotor based on proof by negation

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function

Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep Learning Techniques

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