The main purpose of this study is to find out the relationship between the existing conditions in the railway lines, so that by using it we can take a main and initial step to deal with chain failures. When the experts of maintenance and repairs in railway lines know that a failure in a geometric parameter or in general a change in the state of a geometric parameter will affect the condition of other parameters, they can better deal with the failures and make important decisions.
In this study, the studied data were analyzed using data mining methods such as regression, decision tree, Bayesian classification and associative rules. The main approach in this study is to discover the connections in the geometry of railway lines. The steps to reach the results of the research are as follows: (1) collection of railway line failure data, (2) cleaning the collected data, (3) entering the collected data into the mentioned models and (4) discovering the relationship between the conditions of each of the geometric parameters and the breakdowns that occurred on the track.
There is a non-negligible relationship between track gauge (GAU) failure and other geometric parameters. The conditions of the gauge track geometric parameters are most affected by the conditions of the alignment level and twist parameters, that is, failure in these parameters occurs like a chain. Using the results of association rules can be effective in reducing the inspection (according to the explanation in the article).
This paper predicts GAU conditions by analyzing other track parameters. The linear polynomial regression method and Bayesian classification are used to identify the fault causing GAU failure. Additionally, GAU failure is estimated based on the failure of other parameters through regression analysis.
