Magnetic flux leakage image classification method for pipeline weld based on optimized convolution kernel

In order to intelligently classify magnetic flux leakage signals, this study proposes a method of magnetic flux leakage image classification based on sparse self-coding. With inputting with the magnetic flux leakage image of the pipe weld, it extracts features automatically from the Convolutional Ne...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2019-11, Vol.365, p.229-238
Main Authors: Yang, Lijian, Wang, Zhujun, Gao, Songwei, Shi, Meng, Liu, Bangli
Format: Article
Language:English
Subjects:
Convolution kernel image
Convolution neural network
Entropy similarity constraint
Feature extraction
Pipeline magnetic flux leakage
Sparse self coding
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Summary:In order to intelligently classify magnetic flux leakage signals, this study proposes a method of magnetic flux leakage image classification based on sparse self-coding. With inputting with the magnetic flux leakage image of the pipe weld, it extracts features automatically from the Convolutional Neural Network (CNN) rather than the artificial extraction process. The network classification ability can be improved through pre-training of the convolution kernel and introducing the sparse constraints and the image entropy similarity constraint rules. The experiment uses 500 images of magnetic flux leakage signals to classify the girth welds and spiral welds. The accuracy of classification is 95.1%, which is superior to the traditional convolution neural network model. Experimental results show that the improved model has good feature extraction ability and generalization ability.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2019.07.083