A Novel and Reliable Track Condition Prediction Model for Condition Based Track Maintenance

Abstract

Recent railway transportation developments throughout the world have demonstrated two main trends, high speed and heavy haul. Both of these have resulted in increased wheel loads due to increased dynamic forces and/or higher weights. It is well known that increased wheel loads result in faster deterioration of the track structure. Consequently, maintenance-of-way departments inspect more frequently to ensure safety and comfort for passengers and reduce the risk of damage to freight. An alternative to more frequent inspections is a track maintenance strategy known as condition based maintenance (CBM). CBM has received considerable attention in other industries such as truck fleet management and power systems facility management. Practices in these fields show that CBM can not only reduce interruption of service but also enhance system reliability. What is more, CBM can also reduce life-cycle costs. Within railroading, CBM is used to schedule preventive rail grinding, but, CBM has not yet found widespread implementation in the maintenance of other track components. The key to effective implementation of CBM is reliable forecasts of future conditions based on prediction models. In this paper, a novel track condition prediction model is presented which may serve as a basis for condition based track maintenance. The model is built on practical knowledge of track condition deterioration. Typically, the model can predict track condition (including isolated geometry exceptions and condition of unit track sections) two to three months in advance, depending on tonnage/frequency of trains. To validate the model, track inspection data were collected from the Jinan bureau of China Railroads. Some analysis of the results of track condition predictions is also presented.