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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Acknowledgement
This work was supported by the National Key Research and Development Program of China under Grant 2017YFB1200700, the special fund of Suzhou-Tsinghua Innovation Leading Action under Grant 2016SZ0202, the Natural Science Foundation of China under Grants 61490701 and the Research and Development Project of Beijing National Railway Research & Design Institute of Signal & Communication Ltd.
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Zhuang, Z., Zhang, G., Dong, W., Sun, X., Wang, C. (2018). Intelligent Fault Detection of High-Speed Railway Turnout Based on Hybrid Deep Learning. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_10
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