Abstract: With the continuous growth of underground rail in large cities, subway vehicles have gradually entered a high-density and heavy-load operation state, which will reduce the safety and stability of subway vehicles and bring more subway accidents. The time and cost of subway maintenance have been increasing day by day. Traditional planned repair and fault repair cannot adapt to the current status of maintenance. In this paper, a method of health assessment of subway vehicles based on decision tree and hierarchical analysis is proposed. The soft decision tree distilled by neural network is used to qualitatively judge whether the subway vehicles are in a healthy state or not. Then, according to the expert scoring and weight calculation, the specific scores of the health status of subway vehicles are quantitatively evaluated by analytic hierarchy process. Experiments show that under the condition of optimal qualitative algorithm, quantitative evaluation of the health status of subway vehicles can effectively predict the health status of subway vehicles, and the method is robust.

Key words: subway vehicle health assessment, decision-making tree, analytic hierarchy process