Abstract
A large quantity of failure data for subway vehicles was collected from long-term field investigations and technical exchanged. These failure data has a guiding significance for preserving subway system. By preprocessing (screening, refining, and classification) the original data and statistical analysis, we establish some selected model, then we use A-D test to verify the degree of fitting in selected model so that we can determine the optimal failure distribution model, and then the reliability characteristic quantities could be calculated by the optimal failure distribution model. These reliability characteristic quantities can predict failure rate, failure number, etc. It can be used to assist proper maintenance scheduling to reduce the occurrence of accidents and significant to important practical guiding.
Highlights
Since the first subway line was put into operation in October 1969, there are more than 20 cities owned their subway systems in China, with a total operating mileage over 2400 km
Wang et al presented the service life estimation method based on the three-parameter Weibull maximum likelihood estimation, respecting to the component wearing of high speed multiple units [3]
By sorting the data based on the number of failures, we found that most failures were related to the auxiliary systems, followed by the traction system, running gear, braking system, and control and diagnostic systems
Summary
Since the first subway line was put into operation in October 1969, there are more than 20 cities owned their subway systems in China, with a total operating mileage over 2400 km. How to analyze and deal with such complex large-scale operation failure data, to ensure the safety of urban rail transit has become a major research topic in the field of subway reliability research. The subway is different from the railway in various aspects, such as the departure intervals, operating cycle, line conditions, the failure position, frequency, and maintenance data. Survival analysis method can effectively solve uncertain failure time interval problems under the mechanism of censored data on subway vehicles, in order to get more reasonable results of reliability analysis. We used survival analysis technology to perform the reliability analysis of the subway vehicles for the purpose of accurately grasping the working status of key subway systems, including identifying failures, performing maintenance, and securing the subway’s operation. The survival analysis method has particular advantages in the processing and analysis of censored data during the application of non-parametric, parametric, and semi-parametric survival analysis
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