Quantitative nondestructive testing of wire rope based on pseudo-color image enhancement technology

Zheng, Pengbo; Zhang, Juwei

doi:10.1080/10589759.2019.1590827

Cited by 29 publications

(18 citation statements)

References 13 publications

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“…L is the length of image, W is the width of image. The color moment is the important feature of color images whose color space is not quantified, and the feature vector dimension is low [29]. The three color moments per color component are defined as:…”

Section: Journal Of Sensorsmentioning

confidence: 99%

“…Zhang et al [27,28] utilized wavelet based on compressed sensing to denoise the strand wave, but it restored a lot of noise; then, they combined the Hilbert-Huang Transform (HHT) and Compressed Sensing Wavelet Filtering (CSWF) to reduce various background noises. Zheng and Zhang [29] exploited wavelet soft threshold to inhibit the noise; nevertheless, the denoising effect is poor. Then Zheng and Zhang [30] implemented Variational Mode Decomposition (VMD) and a wavelet transformation to remove noise from the raw MFL signals, which can effectively eliminate noise.…”

Section: Introductionmentioning

confidence: 99%

“…Wan et al [34] investigated the theory on optimal wavelet packet with the Least Squares Support Vector Machine (LS-SVM) to diagnose elevator faults, which was then validated by the experiment. Zheng and Zhang and Qin et al [29,35] took the Support Vector Machine (SVM) with a radial basis function classified to conduct the fault pattern recognition, whereas this method was not very effective.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Magnetic and Infrared Information

Zhang

Jingzhuo

2020

Journal of Sensors

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The lifetime of wire rope is crucial in industry manufacturing, mining, and so on. The damage can be detected by using appropriate nondestructive testing techniques or destructive tests by cutting the part. For broken wires classification problems, this work is aimed at improving the recognition accuracy. Facing the defects at the exterior of the rope, a novel method for recognition of broken wires is firstly developed based on magnetic and infrared information fusion. A denoising method, which is adopted for magnetic signal, is proposed for eliminating baseline signal and wave strand. An image segmentation method is employed for parting the defects of infrared images. Characteristic vectors are extracted from magnetic images and infrared images, then kernel extreme learning machine network is applied to implement recognition of broken wires. Experimental results show that the denoising method and image segmentation are effective and the information fusion can improve the classification accuracy, which can provide useful information for estimating the residual lifetime of wire rope.

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Section: Journal Of Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Magnetic and Infrared Information

Zhang

Jingzhuo

2020

Journal of Sensors

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“…is paper uses the BP neural network model in [17] for comparative verification. Fusion features and magnetic image features are input into the BP neural network.…”

Section: Bp Neural Networkmentioning

confidence: 99%

“…However, the compression sensing structure is complex, the amount of calculation is large, and the filtering of noise is not ideal. Zheng and Zhang [17] used an image processing method that converts the noise-reduced wire rope MFL signal into a grayscale image and then converts the grayscale image into a pseudo color image. e features such as the color moment of the defect image are extracted for recognition, and the recognition accuracy is better than the gray image.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Nondestructive Testing of Wire Ropes Based on Features Fusion of Magnetic Image and Infrared Image

Shiliang

Zhang

2019

Shock and Vibration

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Magnetic flux leakage (MFL) detection is one of the most widely used and best performing wire rope nondestructive testing (NDT) methods for more than a decade. However, the traditional MFL detection has the disadvantages of single source of information, low precision, easy to miss detection, and false detection. To solve these problems, we propose a method of fusion recognition of magnetic image features and infrared image features. A denoising algorithm based on Hilbert vibration decomposition (HVD) and wavelet transform is proposed to denoise the MFL signal, and the modulus maxima method is used to locate and segment the defect. An infrared image acquisition system was designed to collect the infrared image of the surface of the wire rope. Digital image processing techniques are used to segment infrared defect images. The features of the MFL image and the infrared image are extracted separately for fusion. The fusion feature is input into the nearest neighbor (NN) algorithm for quantitative identification, and the same data are input into the backpropagation (BP) neural network for comparison verification. The experimental results show that the fusion of MFL features and infrared features effectively improves the recognition rate of wire rope defects and reduces the recognition error.

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Massive Conscious Neighborhood-Based Crow Search Algorithm for the Pseudo-Coloring Problem

Simplicio Viana,

Contreras,

Pessoa

et al. 2024

Lecture Notes in Computer Science

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Quantitative nondestructive testing of wire rope based on pseudo-color image enhancement technology

Cited by 29 publications

References 13 publications

Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Magnetic and Infrared Information

Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Magnetic and Infrared Information

Quantitative Nondestructive Testing of Wire Ropes Based on Features Fusion of Magnetic Image and Infrared Image

Massive Conscious Neighborhood-Based Crow Search Algorithm for the Pseudo-Coloring Problem

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