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Quantitative Nondestructive Testing of Steel Wire Rope Based on Optimized Support Vector Machine

  • ELECTROMAGNETIC METHODS
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Abstract

In this paper, to address the problems of poor signal noise reduction and low recognition rate in wire rope leakage magnetic detection. We propose the algorithm MSVDW, which uses a combination of median filtering, singular value decomposition (SVD) and wavelet transform, to denoise the collected three-dimensional MFL signals. Then, false color is used to enhance the image. The image is then localized and segmented using the modulus maximum method. The color moments are extracted from the images and used as the input of the particle swarm algorithm optimized support vector machine (PSO-SVM) for training and recognition. The experimental results show that the noise reduction algorithm proposed in this paper effectively reduces the noise of the magnetic leakage signal, the false color image enhances the defect image information, and the algorithm of PSO-SVM greatly improves the recognition rate of defects.

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Funding

This work was partially supported by National Natural Science Foundation of China (Grant no. U2004163).

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Authors and Affiliations

  1. College of Electrical Engineering, Henan University of Science and Technology, 471023, Luoyang, China

    Bing Li, Juwei Zhang & Qihang Chen

  2. Henan Province New Energy Vehicle Power Electronics and Power Transmission Engineering Research Center, Henan University of Science and Technology, 471023, Luoyang, China

    Bing Li, Juwei Zhang & Qihang Chen

Authors
  1. Bing Li
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  2. Juwei Zhang
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Correspondence to Juwei Zhang.

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Bing Li, Zhang, J. & Chen, Q. Quantitative Nondestructive Testing of Steel Wire Rope Based on Optimized Support Vector Machine. Russ J Nondestruct Test 57, 1008–1017 (2021). https://doi.org/10.1134/S106183092111005X

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