Rail Asset Mana­gement 4.0

The rail asset data analytics platform combines data integration, data analytics and decision management into one framework to accelerate your time-to-market.

Within the last few years, more and more sensors and monitoring systems have been rolled out in the rail domain. Bringing all the currently available data together and combining these with new approaches and technologies has revolutionized how infrastructure is maintained: Imagine, now you can spot problems before they occur.

Predictive maintenance models and approaches help to eliminate "too early" (preventive) and "too late" (corrective) interventions. The key intuition is suggesting the ideal time and measure for maintenance work using all data. This way, diagnostic data is not only used as a control function, but also as a driver for maintenance activities. This is a clear paradigm shift, offering several benefits:

  • Change from a reactive maintenance mode to a proactive one.
  • Visibility on the health of the asset.
  • Delivers support to intervention teams, helping them take the right decisions (grind, stuff, mill or change a rail).
  • Reduce high costs by optimizing maintenance interventions (no unscheduled maintenance, long-, medium- or short-term maintenance planning, combining maintenance measures within one intervention, …). 
  • Determine the root cause of failures.
  • Better use of warranty recoveries.
  • Financial forecasting.

ELCA provides a rail asset data analytics service that combines the functionalities of data integration, analytics and decision management into one framework in order to accelerate your time-to-market. The rail asset analytics service does not just includes IT. The real challenge is to integrate customer-specific data – mostly very complex data, hard to understand and interpret correctly – and to consequently apply the right concepts:

  • Huge amounts of influencing parameters such as topology, charge, substructure, weather, dilatation, etc.
  • Vast amounts of (complex) data; diverse models, diverse formats; high performance storage capacity and clustering required.