A Bayesian network model for prediction of weather-related failures in railway turnout systems
作者：
Highlights：
• A failure prediction model based on Bayesian network is proposed.
• An Entropy Minimization-Based method is presented to discretize model variables.
• Learning parameters from small data sets by using a causal noisy MAX model.
• The causal relationship between the weather and failures of turnouts is captured.
• Experiments indicate that the proposed method outperforms other algorithms.
摘要
•A failure prediction model based on Bayesian network is proposed.•An Entropy Minimization-Based method is presented to discretize model variables.•Learning parameters from small data sets by using a causal noisy MAX model.•The causal relationship between the weather and failures of turnouts is captured.•Experiments indicate that the proposed method outperforms other algorithms.
论文关键词：Railway turnouts,Failure prediction,Bayesian network,Causal noisy MAX,Weather-related failure
论文评审过程：Received 2 June 2016, Revised 5 October 2016, Accepted 6 October 2016, Available online 20 October 2016, Version of Record 29 October 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.011
