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A New Feature Evaluation Algorithm and Its Application to Fault of High-Speed Railway Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Algorithm of Speed-up Turnout Fault Intelligent Diagnosis Based on BP Neural Network

Authors : Kai Zhang, Yongfeng Ju, Kai Du, Xu Bao

Published in: Proceedings of the Second International Conference on Intelligent Transportation

Publisher: Springer Singapore

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Abstract

Based on analysis of action current curves change law when the speed-up turnout is normal and fault, this paper summarized the current curve eigenvalues, proposed the turnout fault intelligent self-diagnostic algorithms based on change characteristics of the turnout action current curve. Then mapping sample set between action current curve eigenvalues and turnout fault types, and using BP neural network to establish speed-up turnout fault intelligent diagnosis algorithm. The results show that: the fault diagnosis algorithm of the speed-up turnout is high precision and adaptability.

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previous chapter Weather-Responsive Freeway Speed-Limits Using Approximated Friction Coefficient of Road Surface
next chapter Research and Application of Traffic Visualization Based on Vehicle GPS Big Data
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Metadata
Title
Algorithm of Speed-up Turnout Fault Intelligent Diagnosis Based on BP Neural Network
Authors
Kai Zhang
Yongfeng Ju
Kai Du
Xu Bao
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of the Second International Conference on Intelligent Transportation

Print ISBN: 978-981-10-2397-2

Electronic ISBN: 978-981-10-2398-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-2398-9_26

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