Visual Sensing Inspection for the Surface Damage of Steel Wire Ropes With Object Detection Method

Zhou, Ping; Zhou, Gongbo; Wang, Shihao; Wang, Hanyu; He, Zhenzhi; Yan, Xiaodong

doi:10.1109/jsen.2022.3214109

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…i n represents the number of samples of class i; ih x represents CNN-features of the h-th sample of the i-th category; ClassNum is the number of categories. According to Eq (12), the central feature points CenterPoint of six kinds of broken wire damage is calculated respectively. According to Eq (13), the average inter-class distance of these six central feature points is calculated, called…”

Section: Impact Of Distance Lossmentioning

confidence: 99%

“…Huang X et al [11] designed a new convolutional neural network to realize the damage detection of wire rope. Zhou P et al [12] realized the location and identification of wire rope damage by improving YOLOv3. The above examples provide some meaningful methods for wire rope damage detection, but some problems still need to be solved: (1) There is still a lack of accurate and efficient methods to extract the damaged area.…”

Section: Introductionmentioning

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: Impact Of Distance Lossmentioning

confidence: 99%

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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“…Unfortunately, visual inspection is often insufficient to visualize all defects [62]. Machine vision support (the machine vision method) [63,64] or thermal imaging studies are often required [65]. Termovision tests are also used to determine the service life of wire rope [66] and evaluate geometric parameters [67].…”

Section: Visual Inspection/thermal Imagingmentioning

confidence: 99%

A Comprehensive Review of Steel Wire Rope Degradation Mechanisms and Recent Damage Detection Methods

Mazurek

2023

Sustainability

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Steel wire ropes are the vital load-bearing element in many rope transport devices, such as mine hoists, personal lifts, bridges and cableways. Non-destructive fault detection is a crucial issue for safety and reliability. This paper presents a comprehensive review covering three areas: damage mechanisms for steel wire ropes, physical phenomena used for diagnostics of steel wire ropes and practical applications of magnetometers. The advantages and disadvantages of each group of sensors, such as the induction coil, Hall element, magnetoresistance and optically pumped magnetometers, are presented. The author indicates the direction of the development of signal analysis techniques. In summary, the challenges and future directions for the development of wire rope flaw detection in practical applications are presented, especially considering the future of passive magnetic methods.

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“…In non-contact techniques applied in the steelmaking industry, ultrasonic reverberation spectroscopy was used to detect failures in steel wires. Various experiments were conducted under a magnetic field to enable signal detection to pinpoint the location of the failures [23]. Furthermore, a survey was performed to find defects in the fabric, however it was challenging to explore owing to the extensive stochastic variation, stretching, and distortion of fabric defects influenced by environment factors [24].…”

Section: Introductionmentioning

confidence: 99%

Detection, Verification and Analysis of Micro Surface Defects in Steel Filament Using Eddy Current Principles, Scanning Electron Microscopy and Energy-Dispersive Spectroscopy

Tran,

Shirinzadeh,

Ehrampoosh

et al. 2023

Sensors

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In the current industrial revolution, advanced technologies and methods can be effectively utilized for the detection and verification of defects in high-speed steel filament production. This paper introduces an innovative methodology for the precise detection and verification of micro surface defects found in steel filaments through the application of the Eddy current principle. Permanent magnets are employed to generate a magnetic field with a high frequency surrounding a coil of sensors positioned at the filament’s output end. The sensor’s capacity to detect defects is validated through a meticulous rewinding process, followed by a thorough analysis involving scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). Artificial defects were intentionally introduced into a sample, and their amplitudes were monitored to establish a threshold value. The amplitude signal of these created defect was identified at approximately 10% FSH, which corresponds to a crack depth of about 20 µm. In the experimental production of 182 samples covering 38 km, the defect ratio was notably high, standing at 26.37%. These defects appeared randomly along the length of the samples. The verification results underscore the exceptional precision achieved in the detection of micro surface defects within steel filaments. These defects were primarily characterized by longitudinal scratches and inclusions containing physical tungsten carbide.

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Visual Sensing Inspection for the Surface Damage of Steel Wire Ropes With Object Detection Method

Cited by 20 publications

References 17 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

A Comprehensive Review of Steel Wire Rope Degradation Mechanisms and Recent Damage Detection Methods

Detection, Verification and Analysis of Micro Surface Defects in Steel Filament Using Eddy Current Principles, Scanning Electron Microscopy and Energy-Dispersive Spectroscopy

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