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Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network

Journal of Failure Analysis and Prevention volume 19, pages 1264–1274 (2019)Cite this article

Abstract

In recent years, non-destructive testing methods for wire ropes based on remanence have attracted industry attention. The remanence detection methods have the characteristics of light equipment, high lifting value, high detection precision and low requirements on site conditions. An adaptive filtering algorithm based on wavelet decomposition was proposed to deal with the noise reduction of broken wire rope remanence data. The digital image processing method was used to locate and segment the defect. The texture features, morphological features and seventh-order invariant moments of the defect image were extracted as feature vectors, and an Elman neural network was designed to quantitatively identify the broken wires. The experimental results show that the designed filtering algorithm can effectively suppress the noise in the original signal, and the Elman recognition network has better performance of broken wire recognition.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61040010, 61172014, U1504617), the Key Technologies R&D Program of Henan Province (Grant No. 152102210284), the Science and Technology Program of Henan Education Department (Grant No. 17A510009), the Science and Technology Open Cooperation Program of Henan province (Grant No. 182106000026).

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Authors and Affiliations

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

    JuWei Zhang & ShiLiang Lu

  2. School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    TianYi Gao

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  1. JuWei Zhang
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  2. ShiLiang Lu
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  3. TianYi Gao
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Correspondence to JuWei Zhang or ShiLiang Lu.

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Zhang, J., Lu, S. & Gao, T. Quantitative Detection of Remanence in Broken Wire Rope Based on Adaptive Filtering and Elman Neural Network. J Fail. Anal. and Preven. 19, 1264–1274 (2019). https://doi.org/10.1007/s11668-019-00709-8

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  • DOI: https://doi.org/10.1007/s11668-019-00709-8

Keywords

  • Non-destructive testing
  • Wavelet decomposition
  • Adaptive filtering
  • Morphological features
  • Elman neural network