Online nondestructive testing for fine steel wire rope in electromagnetic interference environment

Yan, Xiaolan; Zhang, Donglai; Pan, Shimin; Zhang, Enchao; Gao, Wei

doi:10.1016/j.ndteint.2017.07.017

Cited by 42 publications

(11 citation statements)

References 14 publications

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“…In the practical application, for thin WR in strong electromagnetic interference environment, Yan et al [27] proposed an electromagnetic non-destructive testing method. In this method, a simplified magnetic circuit was proposed to excite WR, and the defect detection of thin WR in electromagnetic interference environment was realized.…”

Section: Magnetization 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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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: Magnetization 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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“…The number of samples is 185. According to [25,26,28], three models are set to research the relationship between the number of training samples and the quality of the result. The number of training sample set corresponding to model 1 is 92.…”

Section: Research On Sample Size and Fusionmentioning

confidence: 99%

“…Magnetic flux leakage (MFL) detection of wire rope mainly includes the forward calculation model of MFL detection, pretreatment of MFL signal, and inversion of defect [24]. For example, Yan et al [25] employed a threedimensional finite element method (FEM) to analyze MFL signals. This method provided theoretical guidance for detection signal analysis and hardware design.…”

Section: Introductionmentioning

confidence: 99%

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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“…Hidden layer Output layer efficient nondestructive testing technique for the defected wire rope and plays an important role in the dynamic monitoring of wire rope [26][27][28]. Because of the intricate structure of wire rope, there is a complicated relation between the diverse damages and MFL signals.…”

Section: Input Layermentioning

confidence: 99%

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

Zhao

Zhang

2018

Shock and Vibration

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An improved classification approach is proposed to solve the hot research problem of some complex multiclassification samples based on extreme learning machine (ELM). ELM was proposed based on the single-hidden layer feed-forward neural network (SLFNN). ELM is characterized by the easier parameter selection rules, the faster converge speed, the less human intervention, and so on. In order to further improve the classification precision of ELM, an improved generation method of the network structure of ELM is developed by dynamically adjusting the number of hidden nodes. The number change of the hidden nodes can serve as the computational updated step length of the ELM algorithm. In this paper, the improved algorithm can be called the variable step incremental extreme learning machine (VSI-ELM). In order to verify the effect of the hidden layer nodes on the performance of ELM, an open-source machine learning database (University of California, Irvine (UCI)) is provided by the performance test data sets. The regression and classification experiments are used to study the performance of the VSI-ELM model, respectively. The experimental results show that the VSI-ELM algorithm is valid. The classification of different degrees of broken wires is now still a problem in the nondestructive testing of hoisting wire rope. The magnetic flux leakage (MFL) method of wire rope is an efficient nondestructive method which plays an important role in safety evaluation. Identifying the proposed VSI-ELM model is effective and reliable for actually applying data, and it is used to identify the classification problem of different types of samples from MFL signals. The final experimental results show that the VSI-ELM algorithm is of faster classification speed and higher classification accuracy of different broken wires.

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Online nondestructive testing for fine steel wire rope in electromagnetic interference environment

Cited by 42 publications

References 14 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

Quantitative Nondestructive Testing of Broken Wires for Wire Rope Based on Magnetic and Infrared Information

Theory and Numerical Analysis of Extreme Learning Machine and Its Application for Different Degrees of Defect Recognition of Hoisting Wire Rope

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