Quantitative Nondestructive Testing of Wire Rope Using Image Super‐Resolution Method and AdaBoost Classifier

Li, Jigang; Zhang, Juwei

doi:10.1155/2019/1683494

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…Another avenue of research has explored the application of the maximum entropy method to enhance the quality of ultrasonic images, resulting in reduced structural noise of cracks and improved echo signal length [4]. A deep learning DL model was developed to reconstruct ultrasonic Lamb wave images with high spatial resolution [5]. In conjunction with non-subsampled shearlet transform (NSST), super-resolution algorithms have been employed to fuse magnetic flux leakage (MFL) data from double detection boards, further enhancing the quality of crack images [6].…”

Section: Introductionmentioning

confidence: 99%

Development of magnetic image super-resolution model for nondestructive testing

Le,

Luong,

2023

Meas. Sci. Technol.

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Electromagnetic testing (ET) is a method in the nondestructive evaluation (NDE) methodology for detecting and evaluating cracks in engineering structures by measuring the distribution of the electromagnetic field. With the development of advanced deep learning (DL) techniques, DL is getting attention in crack detection problems through the measuring signals of NDE systems. However, obtaining the necessary big data for such experiments is a time-consuming and expensive task. Moreover, the quality of the crack detection results heavily depends on the quality and spatial resolution of the input image data. In this research, we solve the above-mentioned problems by proposing a DL-based method on the magnetic image for both the image super-resolution and image denoise. We first build a magnetic image simulation framework for the ET system, and thus, it is possible to build a large dataset for training the DL model. Second, we build a DL model for enhancing the quality of the magnetic image in both the spatial resolution and the denoise signal. This approach helps to reduce the cost of experiments (i.e. reducing the number of sensors for each experiment), sensor fabrication process, and noise removal of the ET system. We evaluate the proposed approach on several cracks with different sizes and shapes of aluminum specimens. The evaluation metrics, such as the structural similarity index measure (SSIM) and root mean square error show good performance of the proposed approach and compared to traditional methods.

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Section: Introductionmentioning

confidence: 99%

Development of magnetic image super-resolution model for nondestructive testing

Le,

Luong,

2023

Meas. Sci. Technol.

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“…Gongbo Zhou et al [28] proposed a convolutional neural network-based hoisting wire rope inspection method, which could detect various faults such as the broken strand and twisted ropes automatically in real-time, and compared with k-nearest neighbor and an artificial neural network with back propagation. Moreover, other intelligent processing methods may also include the artificial neural network [29], support vector machine [30], AdaBoost classifier [31], and deep learning algorithms [32], which all improved the defect detection accuracy for steel wire rope.…”

Section: Introductionmentioning

confidence: 99%

Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope

Liu

Sun

et al. 2022

Applied Sciences

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As one of the most important processes in steel wire rope inspection, defect signal processing is of great significance in guaranteeing safety and precision measurement. Aiming at the weak signal detection of steel wire rope with mixed strands and noise, a combined signal processing method based on magnetic flux leakage testing and multi-step filtering techniques are proposed in this paper. The experiments are first introduced and performed on three typical types of steel wire rope with diameters of 28 mm, 32 mm, 45 mm, and different wire broken defects detected under liftoff distances of 13 mm and 20 mm; the acquired signals are then analyzed both in time and frequency domain. According to the weak signal characterizations, the principle of the proposed methods and algorithm are given concretely. Afterwards, comparison of signal processing results between the traditional lowpass filtering, wavelet denoising, median filtering, and the proposed method are presented. Finally, the influence factors of smoothing types and moving average span of the proposed methods are investigated. The processing results of the proposed methods are shown through short-time Fourier transform and signal-to-noise ratio analysis, which not only demonstrates the validity and feasibility of the combined methods with the highest signal to noise ratio of 90.37 dB, but also exhibits a great potential of precision defect detection and practical application in steel wire rope inspection.

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“…To solve this problem, some scholars have conducted representative studies. Li, J. et al designed a nondestructive wire rope inspection device which used double detection plates to collect MFL data, improved the image resolution based on a super-resolution algorithm, and finally used the AdaBoost classifier to classify the defect images [ 12 ]. Zhang, J. designed a device based on a residual magnetic field device, proposing a novel filtering system to improve the signal-to-noise ratio, and at the same time used digital image processing techniques to achieve the quantitative recognition of defect images [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Identification of Internal and External Wire Rope Damage Based on VMD-AWT Noise Reduction and PSO-SVM

Tian

Wang

et al. 2022

Entropy

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As a common load-bearing component, mining wire rope produces different types of damage during a long period of operation, especially in the case of damage inside the wire rope, which cannot be identified by the naked eye, and it is difficult to accurately detect such damage using the present technology. In this study we designed a non-destructive testing device based on leakage magnetism, which can effectively detect the internal defects of wire rope damage, and carried out simulation analysis to lay a theoretical foundation for the subsequent experiments. To address the noise reduction problem in the design process, a variational mode decomposition–adaptive wavelet thresholding noise reduction method is proposed, which can improve the signal-to-noise ratio and also calculate the wavelet energy entropy in the reconstructed signal to construct multi-dimensional feature vectors. For the quantitative identification of system damage, a particle swarm optimization–support vector machine algorithm is proposed. Moreover, based on the signal following the noise reduction step, seven different feature vectors, namely, the waveform area, peak value, peak-valley value, wavelet energy entropy classification, and identification of internal and external damage defects, have been determined. The results show that the device can be used to effectively identify internal damage defects. In addition, the comparative analysis showed that the algorithm can reduce the system noise and effectively identify internal and external damage defects with a certain superiority.

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Quantitative Nondestructive Testing of Wire Rope Using Image Super‐Resolution Method and AdaBoost Classifier

Cited by 11 publications

References 26 publications

Development of magnetic image super-resolution model for nondestructive testing

Development of magnetic image super-resolution model for nondestructive testing

Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope

Quantitative Identification of Internal and External Wire Rope Damage Based on VMD-AWT Noise Reduction and PSO-SVM

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