Abstract: Decision trees are widely used as predictive models in the field of machine learning and data mining, and their output is easy to understand and explain. The onboard equipment of high-speed railway has problems such as large streaming data, complicated equipment failure and low diagnostic efficiency. According to the characteristics, the CVFDT decision tree algorithm is proposed to build an intelligent fault prediction model for vehicle equipment (low probability, high probability and failure) by machine learning of the normalized column control device stream data. It becomes “pre-exclusion” of potential equipment failures, improving fault classification accuracy, positioning and diagnostic accuracy, and ensuring high-speed railway operation safety and transportation efficiency.

Key words: high-speed railway, streaming data, onboard equipment, CVFDT algorithm