Maintenance redefined
No more unnecessary depot stops. Trains are constantly monitored during operation, in this way providing the data required to define and plan required condition-based and predictive maintenance measures. At our Rail Service Centers, maintenance planning and execution are optimized according to lean principles for shortest throughput times and the sustainable use of resources. Spare parts are made available by either high-speed delivery or 3D printing. This integrated approach reduces maintenance costs by 20-50% and ensures system availability rates of up to 100%.
The power of data unleashed
The digital depot pays off: Maintenance and servicing costs are sustainably lower. Operators can optimally plan the deployment of their vehicle fleet with reliable condition data. The utilization rate of the individual trains is noticeably increased, and the availability of the entire system reaches its optimum. Data collected from the track and the train in operation are analyzed with the help of Railigent X and forwarded to the digital depot for optimal advance planning. In this way material procurement, resources, and work processes can be optimally coordinated in the depot while the train is still in operation for weeks. The result: maintenance in minimum time, which translates into sustainably lower costs and availability rates of up to 100%.
Lean, smart, aligned maintenance processes
In the digital depot and beyond, numerous processes linked by Railigent X are seamlessly interlocked. The data-based operating principle ensures fast, resource-efficient, consistently optimized processes and contributes to the highest possible system availability.
Procurement of spares
High-speed logistics, the use of additive manufacturing processes, and the prompt replenishment of standard parts kept in the depot ensure reliable availability at the time of maintenance and servicing.
Continuous data collection
Numerous real-time operational, diagnostic, and sensor data are continuously collected on the train in operation. Each train generates more than a billion data points per year.
Data transmission and analysis
All data are transmitted to the cloud in real time, where they are analyzed with the help of the cloud application suite Railigent using algorithms based on Siemens Mobility’s accumulated domain know-how.
Remanent life assessment
Precise analysis renders exact information about the health status of the trains’ components. This enables condition-based, predictive maintenance and the appropriate advance planning of scheduled depot stops.
Data to depot
All relevant data are transmitted to the depot. The necessary maintenance work can be planned in the digital depot while the train is still in operation.
Maintenance execution
The maintenance teams working in parallel on the train have all the information about the condition and necessary work digitally available when the train enters the depot. All work is digitally documented during execution in compliance with standards.
Automated inspection on entry
When the train enters the depot, an automatic vehicle inspection provides the maintenance technicians with additional information about wear on the components specifically associated to the running gear.
Final inspection
Before the train leaves the depot to resume safe and reliable operation, a final inspection is performed.
Advance planning
Staff deployment, resources, and work processes are coordinated in advance and continuously updated based on new incoming data. This ensures streamlined maintenance activities with all necessary resources readily available when the train returns to the depot.
High-speed logistics, the use of additive manufacturing processes, and the prompt replenishment of standard parts kept in the depot ensure reliable availability at the time of maintenance and servicing.
Numerous real-time operational, diagnostic, and sensor data are continuously collected on the train in operation. Each train generates more than a billion data points per year.
All data are transmitted to the cloud in real time, where they are analyzed with the help of the cloud application suite Railigent using algorithms based on Siemens Mobility’s accumulated domain know-how.
Precise analysis renders exact information about the health status of the trains’ components. This enables condition-based, predictive maintenance and the appropriate advance planning of scheduled depot stops.
All relevant data are transmitted to the depot. The necessary maintenance work can be planned in the digital depot while the train is still in operation.
The maintenance teams working in parallel on the train have all the information about the condition and necessary work digitally available when the train enters the depot. All work is digitally documented during execution in compliance with standards.
When the train enters the depot, an automatic vehicle inspection provides the maintenance technicians with additional information about wear on the components specifically associated to the running gear.
