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Train washing station: predictive maintenance

Intelligent sensor technology for condition monitoring and predictive maintenance in train washing systems

The train washing plant of the Société Nationale des Chemins de Fer Luxembourgeois (CFL) cleans local and long-distance trains daily in its 200 metre-long washing hall. To ensure that the wash trolleys travel on both sides of a train, several energy chains are used to safely guide all hoses and cables for energy, data, compressed air, water and cleaning agents. Previously, this had led to costly chain failures more and more often. This is why energy chains are used today together with intelligent sensor technology that monitors the condition of the system and plans maintenance in advance.


  • What was needed: polymer energy chains with sensors for intelligent condition monitoring (i.Sense EC.P) and predictive maintenance (i.Cee)
  • Requirements: as many breakdowns had previously led to high costs and train cancellations, the operator was looking for a solution that would avoid unplanned breakdowns.
  • Industry: transport
  • Success for the customer: thanks to the polymer energy chains for long travels in combination with intelligent sensors, the operator always knows the remaining service life of the system and can anticipate failures before they occur.

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The train washing station of the Société Nationale des Chemins de Fer Luxembourgeois (CFL) is now the most modern in Europe. It cleans local and long-distance trains every day in its 200m wash hall, where a sophisticated system is used.
In order to be able to efficiently wash different train lengths in the large hall, the 200 metres are divided into 66-metre-long segments where wash trolleys are parked. On each side of a train there are four wash trolleys, which travel along the 66-metre-long sections of the train and clean the entire length.
At the core of this process are energy chains that enable the wash trolleys to move in the first place. They have the task of safely guiding the multitude of hoses and cables for energy, data, compressed air, water and cleaning agents.
The system must function reliably in high humidity, wetness and with the use of the chemical cleaning substances.
The high load was evident in a competitor's previous energy chains, which repeatedly caused interruptions in operations as they partially rose up or otherwise failed. This was particularly problematic because the washing plant is operated autonomously and a failure of an energy chain system could bring the entire washing process to a halt. The necessary repair measures were time-consuming and sometimes became necessary at night and on weekends. If the energy chain failed, the possibility of a rail vehicle being locked up in the wash bay could not be ruled out, which could mean the failure of several train runs.


The train company CFL realised that it needed an energy chain system that not only works reliably, but also ensures predictive operation without surprises.
The answer was found in polymer energy chains designed for long travels and equipped with intelligent sensors for condition monitoring and predictive maintenance.
The condition monitoring (i.Sense EC.P) allows permanent monitoring of the e-chain solution by measuring the push and pull forces. If, for example, the chain falls out of the guide trough or rises up, the technology registers this, stops the system and can avoid expensive total damage.
In addition, there are sensors for the predictive maintenance (i.Cee) of the system. It gives the operator an estimate of the service life of the system, taking into account condition monitoring data such as temperature, travel speed or distance travelled, and provides maintenance and inspection instructions.
Today, the interaction of i.Sense EC.P and i.Cee allows the operator not only to avoid total damage, but also to know exactly when damage will occur and how to avoid it. Unforeseen operational failures are a thing of the past.

smart plastics dashboard
Push/pull force in the online dashboard
i.Cee dashboard
e-chain speed via the dashboard

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