Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Multi-Image Fusion

Zhang, Zengguang; Zhang, Juwei

doi:10.1134/s1061830922070117

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…Detecting the edge of the wire rope Setp 1: Edge detection. The Gammar correction [7] is used to uniform illumination and reduce local illumination mutations caused by exposure problems (The grayscale stretching factor  for gamma correction is set to 0.5). Then, the Sobel operator [6] is used to detect the edge of the wire rope and obtain the binary image of the wire rope.…”

Section: Image Preprocessingmentioning

confidence: 99%

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Damage Detection of wire rope Broken wire Damage Based on deep Semi- supervised Learning

zhang,

Ye,

Chen

2024

Preprint

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Aiming at the problems such as the existence of a large amount of background information for wire rope image, the lack of efficient and accurate damage area extraction methods. A novel wire rope image preprocessing method is proposed, which can automatically adjust the pose of wire rope, remove the background information of wire rope image, and extract the damage area of wire rope. In response to the problem that deep learning based wire rope damage detection methods require a large amount of labeled data, a convolutional neural network model WR-CNN adapted to the steel wire rope dataset is designed, And combine WR-CNN with the semi-supervised learning example provided by Temporal Ensembling to form WR-TE-CNN. Aiming at the problem that the existing detection methods of broken wire damage do not consider the difference information between different types of damage and the similar information between the same type of damage, the distance loss function is introduced into the loss function of WR-TE-CNN and the characteristic quantity of calculating distance loss function is improved ( Before the improvement it was called WR-TE-DLCNN, after the improvement it was called WR-TE-IDLCNN ). The experimental results show that When the proportion of labeled data is 0.5, the wire breakage recognition rate of WR-TE-IDLCNN exceeds WR-CNN and is 99.04%. WR-TE-IDLCNN can further utilize the information between different types of damage and between the same types on the basis of WR-TE-DLCNN.

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Section: Image Preprocessingmentioning

confidence: 99%

“…Zhou P et al [6] adjusted pose of the wire rope by detecting the boundary line of the wire rope. Zhang Z et al [7] improved the exposure of damaged images by the Gamma correction. However, how to accurately and efficiently extract damaged areas urgently needs to be solved.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of wire rope Broken wire Damage Based on deep Semi- supervised Learning

zhang,

Ye,

Chen

2024

Preprint

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Quantitative Study on Cross Section Damage of Steel Wire Rope Based on Magnetic Signal Characteristics Under Weak Magnetic Excitation

Zhang,

Liu

2024

IEEE Trans. Instrum. Meas.

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Quantitative detection of wire rope damage based on local structural characteristics

Ye,

Zhang,

Chen

2023

IFS

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Different number of broken wires produce different grooves on the surface of steel wire rope. Based on the local structural features of these grooves, a new broken wire identification method is proposed. By comparing the processing effects of various image enhancement methods, a processing method called adaptive histogram equalization is selected to process the broken wire image. Aiming at a large amount of useless information in structural features extracted by HOG algorithm, a encoder-decoder neural network is designed to reduce the dimension of features. In addition, to effectively avoid information loss caused by the output layer of the BP neural network, a joint algorithm of the BP neural network and the support vector machine is proposed. The experimental results show that using image enhancement technology to process broken wire images can effectively improve the recognition rate of broken wires; The structural features extracted by HOG algorithm are more beneficial to the quantitative recognition of broken wires than the texture features extracted by LBP operator; Compared with various dimensionality reduction methods, neural network can retain more effective information; The joint algorithm can improve the recognition rate of broken wire by at least 0.25% on the basis of BP neural network.

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Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Multi-Image Fusion

Cited by 4 publications

References 20 publications

Damage Detection of wire rope Broken wire Damage Based on deep Semi- supervised Learning

Damage Detection of wire rope Broken wire Damage Based on deep Semi- supervised Learning

Quantitative Study on Cross Section Damage of Steel Wire Rope Based on Magnetic Signal Characteristics Under Weak Magnetic Excitation

Quantitative detection of wire rope damage based on local structural characteristics

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