Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model

The present work aimed at the problems of less negative samples and more positive samples in rail fastener fault diagnosis and low detection accuracy of heavy manual patrol inspection tasks. Exploiting the capacity of a Convolution Neural Network (CNN) to process unbalanced data to solve tedious and...

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Bibliographic Details
Main Authors: Dechen Yao, Qiang Sun, Jianwei Yang, Hengchang Liu, Jiao Zhang
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2020/8823050
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