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Rail Grinding 1Project Manager:

Prof. Gopinath Chattopadhyay

Sponsor Organisations:

Partner Research Organisations:

QUT

Staff Members:


Development of good practice for Combined below rail and above rail decision model for reducing cost, risks and enhancing rail-wheel life based on:

Rail Grinding 2Objectives

  1. Rail grinding
  2. Wheel-rail condition monitoring.

Background

Expected industry growth is projected to increase the annual rail freight task by 98.2 billion tonne-kilometres, or 52.9 per cent, to 283.8 billion tonne-kilometres in 2014/15. (Australian rail transport facts, Apelbaum, 2007) This is possible by advances in rail design, increased speed of the carriers, longer trains and heavier axle loads. Ironically the benefits come with a cost due to increase in wear and fatigue leading to early rail replacements and failures.

Rolling contact fatigue (RCF) alone costs European railways around 300 million euros (AUD$ 485 million) per year and these defects account for about 15% of the total cost of maintenance. The total costs of all defects are about 2 billion euros (AUD$ 3.23 billion) per year (Cannon et al., 2003). In recent years, railroads have been purchasing over 500,000 tons of rails per year at an estimated total cost of US $1.25 billion for replacement of worn out and degraded rails.

Rail grinding and lubrication helps in controlling surface fatigue defects, wear and noise if applied properly. It is expected to maintain optimal rail and wheel profile, eliminate corrugations and head-checks, maintain surface topographies Dong et. al [1994], reduce operating and maintenance cost, wheel/rail performance and reduce risks of derailments.

After the unfortunate Hatfield accident in the year 2000, a considerable amount of research work was commissioned in the UK by the then Railtrack and the RSSB to understand the causes of RCF (Rolling contact fatigue) and wear. The fundamentals of the RCF (Rolling contact fatigue) and wear were developed by research group led by Ajay Kapoor and the application of this science into technology was completed by the then AEA Technology Rail.

This project is expected to develop the best practices in rail grinding combining rails and wheels in decision model to avoid or retard rail wheel wear and fatigue under varying operating conditions. A condition monitoring system would be developed to assess the remaining life and suggest proactive measures to avoid interruptions to service, early replacements, derailments and rail disasters.

Outcomes

  • Guideline for
    • Rail grinding condition monitoring
    • Wheel condition monitoring
    • Rail-Wheel interaction monitoring
  • Combined below rail and above rail decision model for enhancing-rail-wheel life. ( Rail and Wheel Life and wear simulation software)
  • Compilation of data from industry partners combined with published and available data from overseas.
    • Profiles
    • Wheel-rail life
    • Operating and maintenance condition
  • Integration simulation software into participants' infrastructure and rolling stock asset management systems.