A novel texture-based damage detection method for wire ropes

Zhou, Ping; Zhou, Gongbo; He, Zhenzhi; Tang, Chaoquan; Zhu, Zhencai; Li, Wei

doi:10.1016/j.measurement.2019.106954

Cited by 37 publications

(24 citation statements)

References 40 publications

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“…Finally, machine learning classifiers (such as support vector machines, artificial neural networks, etc.) are used for training and testing to obtain robust classifiers for state recognition of unknown images [42]. Optical detection method is an efficient non-destructive detection method, which has been developed in the field of WR surface damage detection.…”

Section: Optical Detection Methodsmentioning

confidence: 99%

“…Finally, machine learning classifiers (such as support vector machines, artificial neural networks, etc.) are used for training and testing to obtain robust classifiers for state recognition of unknown images [42]. Based on camera and offline processing algorithm, Vallan et al [43] established a measurement system which measured the change of rope length according to the characteristics of rope twist to detect the damage of rope.…”

Section: Optical Detection Methodsmentioning

confidence: 99%

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A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Zhou

Zhu

et al. 2019

Applied Sciences

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As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

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Section: Optical Detection Methodsmentioning

confidence: 99%

“…Finally, machine learning classifiers (such as support vector machines, artificial neural networks, etc.) are used for training and testing to obtain robust classifiers for state recognition of unknown images [42]. Based on camera and offline processing algorithm, Vallan et al [43] established a measurement system which measured the change of rope length according to the characteristics of rope twist to detect the damage of rope.…”

Section: Optical Detection Methodsmentioning

confidence: 99%

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Zhou

Zhu

et al. 2019

Applied Sciences

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“…Flaw detection is an important and challenging task in many disciplines, such as hot strip mill rolling [ 1 ], the maintenance of bearings in rotary machinery systems [ 2 , 3 ], minerals processing [ 4 ], and the maintenance of steel wire ropes (SWRs) [ 5 ]. In the present paper, a case in point is the steel wire rope, which is widely used in lifting, transportation, and traction systems.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, progressive efforts have been devoted to exploring various methods to detect wire rope damages (e.g., computer vision method, electromagnetic method, ultrasonic guided wave method, X-ray detection method, etc.) in order to guarantee the reliability and safety of wire ropes [ 5 ]. It is known that due to normal friction and abnormal scratch, the early defects mainly occur on the wire rope surface, which often reflects as wear and broken wire damage [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Autoencoder-Based Flaw Detection for Steel Wire Ropes

Zhang

Tang

Zhang

et al. 2020

Sensors

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Visual perception-based methods are a promising means of capturing the surface damage state of wire ropes and hence provide a potential way to monitor the condition of wire ropes. Previous methods mainly concentrated on the handcrafted feature-based flaw representation, and a classifier was constructed to realize fault recognition. However, appearances of outdoor wire ropes are seriously affected by noises like lubricating oil, dust, and light. In addition, in real applications, it is difficult to prepare a sufficient amount of flaw data to train a fault classifier. In the context of these issues, this study proposes a new flaw detection method based on the convolutional denoising autoencoder (CDAE) and Isolation Forest (iForest). CDAE is first trained by using an image reconstruction loss. Then, it is finetuned to minimize a cost function that penalizes the iForest-based flaw score difference between normal data and flaw data. Real hauling rope images of mine cableways were used to test the effectiveness and advantages of the newly developed method. Comparisons of various methods showed the CDAE-iForest method performed better in discriminative feature learning and flaw isolation with a small amount of flaw training data.

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“…Meanwhile, there are many detection methods in the field of NDT of ferromagnetic materials, such as ultrasonic guided wave detection [2], infrared detection [3], optical detection [4], and electromagnetic detection [5]. Among them, electromagnetic detection method is particularly convenient and accurate, which has been widely used in the field of NDT of ferromagnetic materials [6].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Detection of Wire Rope Based on Three-Dimensional Magnetic Flux Leakage Color Imaging Technology

2020

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In the field of nondestructive testing (NDT) of wire rope, magnetic flux leakage (MFL) detection is the most widely used method. The traditional single-dimensional MFL detection system has the disadvantages of large volume, heavy weight, low sensitivity and low recognition rate. In order to overcome these problems, this paper designed the three-dimensional MFL acquisition system based on unsaturated magnetic excitation using the tunnel magnetoresistive (TMR) elements and proposed the threedimensional MFL detection method. We mapped the three-dimensional MFL signals after noise reduction to Red-Green-Blue (RGB) space for color imaging. Then, the localization and segmentation algorithms were used to crop color images of broken wire defects. Finally, the color moment eigenvalues of color images of six types broken wire defects were extracted as the input of the back propagation (BP) neural network to quantitatively identify the broken wires. From the experimental results of the quantitative identification of broken wire defects, it can be seen that it is feasible to use the color images of the threedimensional MFL signals to quantitatively identify the broken wire defects. Moreover, the threedimensional MFL color imaging can effectively improve the recognition rate of broken wires.

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A novel texture-based damage detection method for wire ropes

Cited by 37 publications

References 40 publications

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

Convolutional Autoencoder-Based Flaw Detection for Steel Wire Ropes

Quantitative Detection of Wire Rope Based on Three-Dimensional Magnetic Flux Leakage Color Imaging Technology

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