Railway Vehicle Door Fault Diagnosis Method with Bayesian Network. Chen, R., Zhu, S., Hao, F., Zhu, B., Zhao, Z., & Xu, Y. In 2019 4th International Conference on Control and Robotics Engineering (ICCRE), pages 70–74, April, 2019. doi abstract bibtex Recent years, more attention has been given to fault diagnosis of railway vehicle door system. In order to handle the uncertainties in fault diagnosis of the door system, a fault diagnosis method based on Bayesian Network was proposed. Fault data provided by a subway company was counted. A model based on Bayesian Network of railway vehicle door was built up and the prior probability of failure was calculated. Inputting fault evidence in the Bayesian model, the posterior probability of each fault would be obtained. Simulation experiment and engineering application show that Bayesian Network can reason through the fault of door system correctly and the result can provide reference and advice for fault diagnosis and maintenance of railway vehicle door.
@inproceedings{chen_railway_2019,
title = {Railway {Vehicle} {Door} {Fault} {Diagnosis} {Method} with {Bayesian} {Network}},
doi = {10.1109/ICCRE.2019.8724211},
abstract = {Recent years, more attention has been given to fault diagnosis of railway vehicle door system. In order to handle the uncertainties in fault diagnosis of the door system, a fault diagnosis method based on Bayesian Network was proposed. Fault data provided by a subway company was counted. A model based on Bayesian Network of railway vehicle door was built up and the prior probability of failure was calculated. Inputting fault evidence in the Bayesian model, the posterior probability of each fault would be obtained. Simulation experiment and engineering application show that Bayesian Network can reason through the fault of door system correctly and the result can provide reference and advice for fault diagnosis and maintenance of railway vehicle door.},
booktitle = {2019 4th {International} {Conference} on {Control} and {Robotics} {Engineering} ({ICCRE})},
author = {Chen, Ruwen and Zhu, Songqing and Hao, Fei and Zhu, Bin and Zhao, Zhendong and Xu, Youxiong},
month = apr,
year = {2019},
keywords = {Bayes methods, Bayesian Network, Fault diagnosis, Fault trees, Logic gates, Maintenance engineering, Rails, Switches, door, fault diagnosis, posterior probability, prior probability, railway, railway vehicle door},
pages = {70--74},
}
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