A Category Classification Based Safety Risk Assessment Method for Railway Wagon Loading Status

Abstract

The identification and control of safety risks in the loading state of goods wagon is one of the important tasks to ensure the safety of goods in transit. In view of the problem that the current risk assessment of transportation schemes is mainly based on manual experience and cannot be quantified, which makes it difficult to accurately determine the safety risk of transportation on the way, a risk assessment method for loading status of goods wagon based on scenario classification was proposed. Firstly, based on a detailed analysis of the safety risk points in various stages of railway freight operations, a SHEL influencing factor model based on scenario classification was constructed. Then, considering the characteristics of railway freight transportation, a fuzzy accident tree model (FTA) of goods wagon loading state risk was constructed, and the fault tree was transformed into a Bayesian network structure according to the mapping algorithm of fuzzy fault tree and Bayesian. Furthermore, a triangular fuzzy membership function was introduced to describe the fault probability of nodes, and a BN based fuzzy fault tree inference algorithm was proposed. Finally, taking a railway station and route transporting coil steel goods in China as an example, this paper explained how to integrate expert knowledge through fault tree and Bayesian network to support railway freight scheme designers in conducting risk quantification assessment of freight wagon loading status