Fault classification based on computer vision for steel wire ropes

Pan, Feiyang; Ren, Liyuan; Zhou, Jianguo; Liu, Zhiliang

doi:10.1088/1742-6596/2184/1/012035

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…In Ref. [14] presented a different approach to detecting local wire rope defects. The proposed method characterized wire rope surface images with a smaller number of feature dimensions and provided a good distinction between different types of rope surface defects.…”

Section: Other Methods Of Diagnosing Steel Wire Rope Damagementioning

confidence: 99%

Detecting Discontinuities in Steel Wire Ropes of Personal Lifts Based on the Analysis of Their Residual Magnetic Field

et al. 2022

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Steel wire rope is essential to many rope transport devices. As steel ropes are used, they become damaged, the identification of which is often very difficult or time-consuming. The criteria for retiring steel wire ropes are rigorous—sometimes, ropes that remain fit for further operation are replaced. This article aims to define a novel method of identifying the condition of steel ropes based on their residual magnetic field measurements and their potential use in other industries in the event of damage. This article presents a methodology for detecting discontinuities in steel ropes, which allows for determination of their suitability for further operation. The work uses a rope as a load-bearing element of a personal lift. The initial signal was recorded with a SpinMeter-3D magnetometer. The obtained results were subjected to the extraction of features, the analysis of which allowed identifying the damage. The obtained results enable us to conclude that this procedure is crucial in the context of sustainable development.

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Section: Other Methods Of Diagnosing Steel Wire Rope Damagementioning

confidence: 99%

Detecting Discontinuities in Steel Wire Ropes of Personal Lifts Based on the Analysis of Their Residual Magnetic Field

et al. 2022

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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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Intelligent visual detection method for the early surface damage of mine hoisting wire ropes

Zhou,

Wang

et al. 2024

Meas. Sci. Technol.

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An intelligent visual detection method is proposed to identify early surface damage in operational mine hoist steel wire ropes (MHWRs), addressing challenges arising from complex surface morphology, subtle early-stage damage, and difficulties in identification. The method is based on an improved YOLOv5 (you only look once) network, a visual detection system has been developed, and on-site experiments and applied research are currently underway. Firstly, the operating conditions of the in-service MHWRs were analyzed. In response to their dynamic hoisting characteristics and complex surface morphology, a detection system framework based on high-speed visual perception and deep intelligent algorithms was proposed. Then, the Retinex and CBAM attention mechanism modules were introduced to solve the problems of uneven illumination and subtle early-stage damage, and a visual recognition network model for detecting surface early-stage subtle damage in MHWRs was constructed on the YOLOv5 base module, referred to as MineWR-net. Subsequently, based on the evolutionary process of surface damage in MHWRs, a dataset for early-stage damage was established, and performance comparative studies were conducted on various object detection algorithms. Finally, integrating practical operating conditions and application requirements, on-site experimental research was carried out for the MHWRs visual detection system. The results indicate that the designed MHWRs visual inspection system can achieve dynamic and clear acquisition of the surface image of serving steel wire rope under high-speed movement. Compared with other target detection algorithms, the average detection accuracy (Map) of MineWR-net is 82.3%, which has superior detection performance. This research can provide technical support for the industrial application deployment of the healthy operation and maintenance of MHWRs.

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Fault classification based on computer vision for steel wire ropes

Cited by 5 publications

References 13 publications

Detecting Discontinuities in Steel Wire Ropes of Personal Lifts Based on the Analysis of Their Residual Magnetic Field

Detecting Discontinuities in Steel Wire Ropes of Personal Lifts Based on the Analysis of Their Residual Magnetic Field

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

Intelligent visual detection method for the early surface damage of mine hoisting wire ropes

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