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Intelligent maintenance – civity supports Banedanmark in the development of a new maintenance and servicing strategy

European infrastructure managers (IM) face major challenges and decisions in asset management: Ageing infrastructure and extreme weather events significantly increase maintenance cost. Funding budgets are tight. New data-technology emerges, offering solutions to just these economic problems. Sensors, drones, cameras on trains, current & voltage measurements, lidar, radar, all collect data more easily than traditional inspection ever did. Rapidly improving analytical and algorithmic data processing and data-decision supports the paradigm shift in maintenance. A shift to more cost-efficient maintenance and condition-data-driven maintenance is facilitated and a way to optimise asset lifetime and increase the infrastructure resilience at the same time seems tangible.

For management the questions are how to start a shift, how much time, budget, and expertise needs to be dedicated and when is a pay-off expected.
In this context, civity Management Consultants supported Banedanmark (BDK) in identifying the best measures to optimize asset management towards the above objectives.

  1. A benchmarking study’s results spot peer IMs’ measures in the field of asset condition data. Examples range from acceleration sensors on pantographs for catenary inspection to video-based sleeper clip analysis and electrical current analysis for intelligent switch monitoring.

  2. For decision support in order to pinpoint the most valuable approaches measures are identified that are:
  • fitting with BDK’s maturity
  • relying on existing in-house expertise or available market solutions
  • aligned with BDK’s asset management strategy and
  • expecting a foreseeable pay-off.
  1. An in-depth look into prioritised options with use cases for the most important systems, showing cost-benefit effects, organisational implications, and an implementation roadmap, provides management with a concrete reliable basis to decide on the introduction or expansion of condition-based, preventive and predictive maintenance.