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2018 | OriginalPaper | Buchkapitel

Intelligent Fault Detection of High-Speed Railway Turnout Based on Hybrid Deep Learning

verfasst von : Zhi Zhuang, Guohua Zhang, Wei Dong, Xinya Sun, Chuanjiang Wang

Erschienen in: AI 2018: Advances in Artificial Intelligence

Verlag: Springer International Publishing

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Abstract

With the purpose of detecting the turnout fault without label data and fault data timely, this paper proposes a hybrid deep learning framework com-bining the DDAE (Deep Denoising Auto-encoder) and one-class SVM (Support Vector Machine) for turnout fault detection only using normal data. The proposed method achieves an accuracy of 98.67% on the real turn-out dataset for current curve, which suggests that this work realizes the purpose of detecting the fault with only normal data and provides a basis for the intelligent fault detection of turnouts.

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Titel: Intelligent Fault Detection of High-Speed Railway Turnout Based on Hybrid Deep Learning
verfasst von: Zhi Zhuang
Guohua Zhang
Wei Dong
Xinya Sun
Chuanjiang Wang
Verlag: Springer International Publishing
Buch: AI 2018: Advances in Artificial Intelligence
Print ISBN: 978-3-030-03990-5

Electronic ISBN: 978-3-030-03991-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-03991-2_10

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