Track Circuit Shunting Booster


TC-S BOOSTER

Project information

  • Acronym: TC-S BOOSTER
  • Track Circuit Shunting Booster
  • Project director: Christian Chavanel
  • Project manager: Jean-Michel Evanghelou
  • Status: completed project
  • Project code: 2018/RSF/583

Project description

  • Guaranteeing the absolute safe detection of any rolling stock by existing track circuits
  • Existing track circuits (TC) are used, in a large majority of countries, as the basis of safe train detection (and/or trackside to train transmission) for signalling safety systems (interlocking, level crossing, block system, etc.). This will continue to be the case for a significant number of years. All measures should be taken to make sure that they function perfectly safely in all situations, as late or non-detection leads directly to an unsafe situation.
  • Preserving the high investments made in the past to equip line and interlocking zones with track circuits
  • Existing track circuits are generally working well, with high safety and availability levels. Only a very small number of them (typically less than 0.1% of the TCs on non-electrified lines) present difficulties with modern rolling stock (bogie dynamic, disk or composite brakes, etc.) and/or difficult condition of use (leaves, traffic reduction on regional lines, etc.). All measures should be taken to improve the sensibility of these specific TCs, without modifying or retuning the existing track circuit (target: below €250 per TC).
  • Reducing TC monitoring and maintenance costs through effective remote monitoring
  • The safe functioning of TCs generally requires cyclic preventive maintenance operations (on-site maintenance) to check the detection capability of the TCs (safety, availability) at the possible extreme conditions of use (rail pollution, climate change effects, etc.). All measures should be taken to improve the availability of TCs and to reduce the need for maintenance operations.
  • Creating the conditions for effective predictive maintenance based on real-time information from maintenance and/or operation teams
    Remote monitoring with data storage of receiver input signals and an artificial intelligence treatment of these signals create the required conditions for predictive maintenance. This treatment could a
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Monday 1 January 2018
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