Research on X-ray in-situ image processing technology for electric power strain clamp

Liu, Yanxiu; Zhao, Peng; Qin, Xulei; Liu, Yanyang; Tao, Ye; Jiang, Sheng; Li, Ye

doi:10.1117/12.2605528

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…It can be seen from the simulation results that the proposed algorithm can obtain the optimal imaging voltage through the calculation for the simultaneous imaging of objects with a large difference in absorption coefficients [40,41].…”

Section: Strain Clamp X-ray Imaging Testmentioning

confidence: 97%

Approximation Algorithm for X-ray Imaging Optimization of High-Absorption Ratio Materials

Liu

Jiang

et al. 2022

Symmetry

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In the application of X-ray industrial flaw detection, the exposure parameters directly affect the image quality. The voltage of the tube is the most important factor, which is difficult to be accurately calculated. Especially in the detection of a workpiece composed of both high absorption coefficient and low absorption coefficient materials, the improper symmetric balance of the tube voltage would lead to an overexposure or underexposure phenomenon. In this paper, based on the X-ray absorption model, combined with the performance of the X-ray imaging detector, and taking the optimal symmetry and contrast as the model constraint condition, the key factors of high absorption ratio material imaging are decomposed. Through expansion and iteration, the calculation process is simplified, the optimal imaging convergence surface is found, and then the optimal energy input conditions of high absorptivity materials are obtained and symmetrically balanced. As a result, this paper solves the problem of fast selection and symmetric factor chosen of the optimal tube voltage when imaging materials with high absorption ratios. It reduces the subsequent complications of the X-ray image enhancement process and obtains a better image quality. Through experimental simulation and measurement verification, the error between the theoretical calculation results and the measured data was better than 5%.

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Section: Strain Clamp X-ray Imaging Testmentioning

confidence: 97%

Approximation Algorithm for X-ray Imaging Optimization of High-Absorption Ratio Materials

Liu

Jiang

et al. 2022

Symmetry

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“…Strain refers to the local relative deformation of an object when subjected to external forces or non-uniform temperature field. In structural engineering, equipment detection, and health monitoring, daily inspection and maintenance are critical to maintain the service life of equipment [1][2][3][4][5]. Strain monitoring is an excellent means for equipment safety detection and management.…”

Section: Introductionmentioning

confidence: 99%

Research on microstrain loading device and strain detection method based on SWCNT strain sensor

He,

Zheng,

Zhang

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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Monitoring the micro-strain on the surface of equipment is of great significance for its health monitoring and daily maintenance. A micro-strain detection method is researched in this article based on the photoluminescence characteristics of SWCNT materials. The detection of micro-strain can be achieved by using the SWCNT strain sensing film pasted on the surface of the tested equipment. A standard strain gauge can also be pasted on the back of the corresponding position where the SWCNT strain sensing film is pasted to check the detection accuracy of the SWCNT strain sensing film. A micro-strain loading device is designed in this paper. Then, a simulation study on the stress distribution of components with different shapes and sizes is conducted. Simulation results indicate that the geometric shape of the tested component has a significant impact on the stress distribution.

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“…The present deep learning-based inspection solutions for strain clamps are classifying or detecting defective samples, but defective samples are not easy to obtain, and the constructed defective sample library does not include all abnormalities. The existing research focused on single defects with obvious characteristics and the generalization ability needs further verification [3,25,26]. Defect detection methods based on defective samples naturally could not satisfy the real demands.…”

Section: Introductionmentioning

confidence: 99%

Live line strain clamp's DR image anomaly detection based on unsupervised learning

Haoliang

Jia

Yuting

et al. 2023

IET Generation Trans & Dist

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Due to the high‐risk working environment of high‐voltage transmission lines, defect samples of strain clamps cannot be fully and completely collected. As a result, the deep learning method based on defect sample tags cannot effectively identify all abnormalities. To solve this problem, an unsupervised anomaly detection method based on knowledge distillation is proposed, which only requires a small number of normal samples to drive the model for anomaly detection. ResNet is the framework of the teacherstudent model, and the feature activation layer after ResBlock is used for knowledge transfer. Residual‐assisted attention and pyramid‐splitting attention were used to enhance the spatial perception and multi‐scale information utilization ability of the model. This model only transmits the information of normal samples and is sensitive to abnormal samples. The proposed model outperformed the baseline by 23% and individual categories by 78% on the MVTec AD (Anomaly Detection Dataset) and outperformed the baseline by 45% and individual categories by 10% on the CIFAR10 and is also reliable for Mnist and Fashion Mnist. This method performs best (82.71%) over the existing method on the self‐built data set.

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Research on X-ray in-situ image processing technology for electric power strain clamp

Cited by 4 publications

References 1 publication

Approximation Algorithm for X-ray Imaging Optimization of High-Absorption Ratio Materials

Approximation Algorithm for X-ray Imaging Optimization of High-Absorption Ratio Materials

Research on microstrain loading device and strain detection method based on SWCNT strain sensor

Live line strain clamp's DR image anomaly detection based on unsupervised learning

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