Research on Auxiliary Power Supply System of High-Speed Train Using Bayesian Network

The “Rejuvenation” high-speed train is an advanced electric multiple unit (EMU) train developed by China independently. When high-speed trains have been put into operation, situations where the auxiliary power supply system cannot be used because of battery system failure are becoming more common. This poses a great threat to high-speed train safety and stability. This paper researches the actual operating state of the auxiliary power supply system of the “Rejuvenation” train. Each sensor of the system is used as network node to construct a Bayesian network. We use the I-ACO-B algorithm to learn structure of Bayesian network, and use the Bayesian parameter estimation algorithm to learn parameters of Bayesian network. Through these two kinds of learning, the auxiliary power supply network, can be constructed. Finally, the network can be verified by actual data.