Nondestructive and quantitative evaluation of wire rope based on radial basis function neural network using eddy current inspection

Qingsong, Cao; Liu, Dan; Yuan, He; Jihui, Zhou; Codrington, John

doi:10.1016/j.ndteint.2011.09.015

Cited by 30 publications

(11 citation statements)

References 17 publications

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“…Only a few literature reports involve other non-destructive detection methods of WRs, such as eddy current detection [73,74] and ray detection [75,76], and are in the laboratory stage for many reasons. The detection principles are depicted in Figure 6.…”

Section: Other 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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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: Other 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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“…RBF neural network [10,13] is better than BP in convergence rate learning and local approximation performance, because minority weights will locally influence net output in local input sets. Thus, RBF was successful in applying an approximation of nonlinear function, time series analysis, data analysis, pattern recognition, information processing, image processing, system modeling, controlling, and fault diagnosis.…”

Section: Classificationmentioning

confidence: 99%

Evaluation of Composite Wire Ropes Using Unsaturated Magnetic Excitation and Reconstruction Image with Super-Resolution

Tan

Zhang

2018

Applied Sciences

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Abstract:Estimating the exact residual lifetime of wire rope involves the security of industry manufacturing, mining, tourism, and so on. In this paper, a novel testing technology was developed based on unsaturated magnetic excitation, and a fabricating prototype overcame the shortcomings of traditional detection equipment in terms of volume, sensibility, reliability, and weight. Massive artificial discontinuities were applied to examine the effectiveness of this new technology with a giant magneto resistance(GMR) sensor array, which included types of small gaps, curling wires, wide fractures, and abrasion. A resolution enhancement method, which was adopted for multiframe images, was proposed for promoting magnetic flux leakage images of a few sensors. Characteristic vectors of statistics and geometry were extracted, then we applied a radial basis function neural network to achieve a quantitative recognition rate of 91.43% with one wire-limiting error. Experimental results showed that the new device can detect defects in various types of wire rope and prolong the service life with high lift-off distance and high reliability, and the system could provide useful options to evaluate the lifetime of wire rope.

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“…Permanent magnet is characterized by small volume, low cost, lightweight, high magnetic field, not requiring power, and easy to dispose and install. e MFL signals are gathered by some arrays of Hall effect sensors disposed at the circumference clinging to the outer surface of wire rope [29]. So the MLF signals are influenced by the lift-off distance, velocity effect, shaking, and various properties of the defects.…”

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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Nondestructive and quantitative evaluation of wire rope based on radial basis function neural network using eddy current inspection

Cited by 30 publications

References 17 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

Evaluation of Composite Wire Ropes Using Unsaturated Magnetic Excitation and Reconstruction Image with Super-Resolution

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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