Rail Track Maintainance Prediction

In general, rail life is analysed segment-by-segment with each based on the optimum length required for effective analysis and specific track and traffic conditions. The segments are assumed to be homogeneous in that the rail life and certain key parameters will be the same for the entire segment. The rail fatigue life forecasting algorithm uses Weibull analysis techniques to predict defect growth rates and future defect levels. Studies have shown that rail develops fatigue defects as a function of the cumulative traffic that passes over it, in addition to factors such as axleload, wheel-rail contact, and rail metallurgy and cleanliness. The rate of defect formation and accumulation with traffic has been shown to follow a Weibull distribution, which is in the form of a logarithmic relationship. Thus, as the rail ages, the expected rate of defect occurrence increases significantly, corresponding to the logarithmic nature of the Weibull equation. Since complete fatigue and tonnage data is not always available, effective models, such as RailLife, employ a hierarchy of analysis approaches, which are directly related to the actual amount of available data. The output of the algorithms is an annual forecast of defect rate (defects/km/year) and cumulative defects for each segment, together with the forecast life of the rail. Rail life forecasts are based on user defined rail replacement criteria which are usually characterised by the number of defects, particularly fatigue related defects, which occur within a defined period of time, usually a year. This is the point at which it is most appropriate to replace the rail.

 

Rail grinding management software is used to manage the removal or control of rail surface defects and to maintain the rail profile, which in turn affects both rail fatigue and wear rates. Research has shown that rail grinding is extremely effective in reducing the rate of fatigue defect development and extending the rail life. Control of the wheel-rail interface through grinding has also been shown to be effective at reducing the rate of rail wear. Software tools are used to manage, plan, and monitor rail grinding and management of the rail profile. This includes comparison of actual and desired rail profile or template (Figure 3) and the development of a curve-by-curve grinding plan, which defines the number of grinding passes, pattern, and grinding speed.