Application of Variational Mode Decomposition and <i>k</i>‐Nearest Neighbor Algorithm in the Quantitative Nondestructive Testing of Wire Ropes

Zheng, Pengbo; Zhang, Juwei

doi:10.1155/2019/9828536

Cited by 15 publications

(12 citation statements)

References 19 publications

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“…Several recognition algorithms are applied to the MFL data: BP neural network [28,29,32], RBF algorithm [27], and KNN algorithm [30]. The data for recognition is the same as that used in the KELM network.…”

Section: Classification Resultsmentioning

confidence: 99%

“…Inspired by [30], the defects are located and segmented using the modulus maximum method. The flaws are divided into images of resolution with 300 * 300.…”

Section: Methodsmentioning

confidence: 99%

“…Result. BP neural network [28,29,32], radial basis function (RBF) algorithm [27], k-nearest neighbor (KNN) [30], and KELM are applied to three models. Table 1 shows the results of fusion for each method under different models.…”

Section: Research On Sample Size and Fusionmentioning

confidence: 99%

“…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. Hong et al [31] proposed an adaptive wavelet threshold denoising method based on a new threshold function, which achieved good denoising effect on the MFL signal of wire rope.…”

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: Classification Resultsmentioning

confidence: 99%

“…Inspired by [30], the defects are located and segmented using the modulus maximum method. The flaws are divided into images of resolution with 300 * 300.…”

Section: Methodsmentioning

confidence: 99%

Section: Research On Sample Size and Fusionmentioning

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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“…e wire rope MFL signal acquisition device under the unsaturated magnetic excitation of the literature [23] is cited herein. As shown in Figure 1, the sensor array is used to collect the MFL information of the wire rope defect.…”

Section: Mfl Data Collectionmentioning

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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A Review of Wire Rope Detection Methods, Sensors and Signal Processing Techniques

2020

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Application of Variational Mode Decomposition and k‐Nearest Neighbor Algorithm in the Quantitative Nondestructive Testing of Wire Ropes

Cited by 15 publications

References 19 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

A Review of Wire Rope Detection Methods, Sensors and Signal Processing Techniques

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