Wagons are generally maintained on a time basis.
This scheme has shown its limitations from both a safety and a performance point of view as it does not allow for the actual condition of each component to be considered nor does it anticipate the occurrence of failures.
Maintenance 4.0 brings a new perspective to the maintenance process, based on the analysis of data from the components installed on the wagon, historical data, empty/loaded mileage, defined wear laws and advanced algorithms. By using new features, we can monitor the actual condition of the wagon components and predict when a wagon needs to be maintained. The implementation of these systems is an important step in improving railway performance and safety. It is through the implementation of these solutions that we will be able to better anticipate our maintenance events and therefore act on the performance of Suppply Chain maintenance.
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The mileage is a basic data required to define wear laws (predictive maintenance). However, simply monitoring mileage is not enough. Therefore, we are developing solutions to qualify mileage: empty or loaded mileage and identification of particularly stressful areas (mountainous areas for example).
Braking is the other key data to predict maintenance events, whether they are related to wear (predictive maintenance) or to the occurrence of defects (conditional maintenance).
We develop solutions to know the braking frequency, the duration of braking and its strengh.
We also develop customised solutions to meet specific need (contact us).
The railway infrastructure is equipped with sensors to ensure the safety of traffic.
The data generated by these sensors is currently communicated to the railway companies but is not available to the keepers. However, this type of system is the most effective for detecting and anticipating failures, for example on axles. We have therefore taken steps at European level to gain access to this data and enrich our models.
