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Wire Rope Defect Recognition Method Based on MFL Signal Analysis and 1D-CNNs.

Shiwei Liu¹, Muchao Chen^2,3

¹College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

A new method uses magnetic flux leakage (MFL) signal analysis and convolutional neural networks (CNNs) for accurate wire rope defect detection. This approach achieves over 98% accuracy, ensuring safety in critical applications.

Keywords:

convolutional neural network (CNN)defect detection feature extraction signal analysis wire rope

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Area of Science:

Engineering
Materials Science
Artificial Intelligence

Background:

Quantitative defect detection in wire ropes is vital for safety across diverse applications.
Complex inspection environments present significant challenges for accurate testing.

Purpose of the Study:

To propose a novel method for wire rope defect recognition using magnetic flux leakage (MFL) signal analysis and convolutional neural networks (CNNs).
To address the difficulties and challenges in accurate wire rope defect testing.

Main Methods:

Analysis of one-dimensional (1D) MFL testing data in time and frequency domains.
Signal denoising using Haar wavelet transform and differentiated operation, followed by normalization.
Feature extraction and classification using 1D-CNNs for defect recognition.

Main Results:

The proposed MFL and 1D-CNNs method achieved the highest testing accuracy, exceeding 98%.
Demonstrated validity and feasibility for quantitative and accurate detection of broken wire defects.
Outperformed six other machine learning methods and related algorithms in performance evaluation.

Conclusions:

The developed method offers a highly accurate and reliable solution for wire rope defect detection.
The approach shows considerable potential for practical applications in ensuring structural integrity.
Limitations and future research directions were also discussed.