01763624

Component

https://annualmeeting.myt...nlineProgram/Details/15394

To predict the train fault probability on urban rail transit main line reasonably, train formation, cumulative running kilometer and un-wheeling repair or overhaul experience are determined as the main influencing factors of train fault probability through qualitative analysis. Subsequently, discrete dataset of fault occurrence number for single train in a certain 120,000 running kilometer period is generated based on the actual data. According to the distribution characteristics of data, Poisson distribution and zero-inflated Poisson distribution are used to build three alternative models with possible function forms. After comparison and selection, a prediction model of train fault probability on urban rail transit main line based on Poisson distribution is finally proposed. The results indicate that train fault probability will increase when train formation increases, and will decrease first and then rise when cumulative running kilometer increases. When a train has been put into use, the minimum train fault probability will appear in the 4th 120,000 running kilometer period, and the initial value will be exceeded in the 7th 120,000 running kilometer period.

This paper was sponsored by TRB committee AR020 Standing Committee on Rail Rolling Stock and Motive Power.

TRBAM-21-01076

English

14p

2021

Transportation Research Board 100th Annual Meeting

Digital/other

Figures; References; Tables

Fault monitoring; Main lines; Multiple unit cars; Poisson distributions; Predictive models; Rail transit; Service disruption; Urban transit

Maintenance and Preservation; Planning and Forecasting; Public Transportation; Railroads; Vehicles and Equipment

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-01076

TRIS, TRB, ATRI

Dec 23 2020 11:07AM