Prediction of Failure Rate of Metro Vehicle Bogie Based on Neural Network
Metro train bogie system is located between the car body and the track, which is one of the key subsystems to ensure the safety of train operation. As a complex system, bogie system is composed of many components, once the failure happened, it would impact the normal operation of the whole train. In order to better predict the failure rate of bogie system, radial basis function (RBF) neural network is introduced to predict the failure rate of the whole system through the fault data of bogie components, and genetic algorithm is used to optimize the model. Experimental results showed that the proposed method can accurately predict the bogie failure rate, and can be used as a system-level reliability prediction method, providing a data basis for later system improvement and optimization.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9789811506437
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Supplemental Notes:
- © Springer Nature Singapore Pte Ltd. 2020.
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Corporate Authors:
Springer Singapore
152 Beach Road
Singapore, 189721 -
Authors:
- Wang, Xiuqi
- Qin, Yong
- Fu, Yong
- Ye, Meng
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Conference:
- 9th International Conference on Green Intelligent Transportation Systems and Safety
- Location: Guilin , China
- Date: 2018-7-1 to 2018-7-3
- Publication Date: 2020-3
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 1079-1088
- Monograph Title: Green, Smart and Connected Transportation Systems: Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety
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Serial:
- Lecture Notes in Electrical Engineering
- Volume: 617
- Publisher: Springer
- ISSN: 1876-1100
Subject/Index Terms
- TRT Terms: Car trucks (Railroads); Failure analysis; Neural networks; Predictive models; Rail transit; Reliability
- Subject Areas: Data and Information Technology; Public Transportation; Railroads; Vehicles and Equipment;
Filing Info
- Accession Number: 01898798
- Record Type: Publication
- ISBN: 9789811506437
- Files: TRIS
- Created Date: Nov 13 2023 9:02AM