Predictive maintenance in practice
Cooperation between Schweizer Zentralbahn and the software specialist ZEDAS
11.02.2020 | At the 17th International Rail Vehicle Conference in Dresden, Gerhard Züger - Head of Production and Rolling Stock from Zentralbahn - and Gritt Hannusch - Head of Asset Management from ZEDAS GmbH - will illustrate approaches for predictive maintenance. The aim of this maintenance strategy is to increase vehicle reliability.
Zentralbahn has taken a closer look at the causes of rail vehicle failures. A frequent reason that prevents further travel during operation is faulty door contacts. The case study of door diagnosis as well as the wheelset wear prognosis will be examined in more detail in the lecture.
Door diagnosis
In a pilot project of Zentralbahn in cooperation with ZEDAS, a train was equipped with sensors for the door end contacts in an OnBoard Unit. All data collected ends up in the central maintenance system zedas®asset. This data is evaluated using diagnostic and analysis tools. If there is a threat of door malfunctions, the driver's cab and the workshop receive a message. Therefore the local personnel can prevent a complete failure by taking precautionary, manual blocking of the door and the maintenance team can take the maintenance into account in the workshop and scheduling plan.
Failures and delays in onward travel are thus prevented and corrective maintenance, which is usually a matter of great urgency, is reduced.
Wheelset wear forecast
The data from wheelsets are also analysed in zedas®asset. Measurement data of wheel profiles can come from a wide variety of systems. The analysis of wheelset data not only enables the visualisation of the current state of a wheelset, but also a forecast. It shows the course of the future condition as well as the point in time when the limit values will be reached. Deadlines for reprofiling and replacement are automatically entered into the maintenance plan by zedas®asset.
Conclusion
Maintenance activities can be prioritized more easily by using asset management software, since the forecasts are based on real-life conditions. This optimized maintenance strategy shortens the time rail vehicles spend in the depot and workshop, resulting in higher availability. Foresighted maintenance allows maintenance resources such as workshop times, personnel and machines to be allocated more evenly.