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

The quantitative defect detection of wire rope is crucial to guarantee safety in various application scenes, and sophisticated inspection conditions usually lead to the accurate testing of difficulties and challenges. Thus, a magnetic flux leakage (MFL) signal analysis and convolutional neural netwo...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-03, Vol.23 (7), p.3366
Main Authors: Liu, Shiwei, Chen, Muchao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
convolutional neural network (CNN)
Data analysis
Deep learning
defect detection
Defects
feature extraction
Inspection
Machine learning
Magnetic flux
Magnetic flux leakage testing
Methods
Neural networks
Noise
Nondestructive testing
Recognition
Sensors
Signal analysis
Signal processing
Stress concentration
Wavelet transforms
Wire
Wire rope
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Summary:The quantitative defect detection of wire rope is crucial to guarantee safety in various application scenes, and sophisticated inspection conditions usually lead to the accurate testing of difficulties and challenges. Thus, a magnetic flux leakage (MFL) signal analysis and convolutional neural networks (CNNs)-based wire rope defect recognition method was proposed to solve this challenge. Typical wire rope defect inspection data obtained from one-dimensional (1D) MFL testing were first analyzed both in time and frequency domains. After the signal denoising through a new combination of Haar wavelet transform and differentiated operation and signal preprocessing by normalization, ten main features were used in the datasets, and then the principles of the proposed MFL and 1D-CNNs-based wire rope defect classifications were presented. Finally, the performance of the novel method was evaluated and compared with six machine learning methods and related algorithms, which demonstrated that the proposed method featured the highest testing accuracy (>98%) and was valid and feasible for the quantitative and accurate detection of broken wire defects. Additionally, the considerable application potential as well as the limitations of the proposed methods, and future work, were discussed.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23073366