30. July 2026, 07:25 o'Clock
Article: Predictive maintenance increases availability
Optimising maintenance, reducing costs and sustainably increasing fleet efficiency through automation.
Predictive maintenance at DB Cargo
DB Cargo uses predictive maintenance to maximize the availability of its locomotive fleet. Data analytics with intelligent algorithms continuously monitor the condition of defined components real-time: 360 degree observability. Maintenance needs are detected at an early stage and fed into workshop planning in a targeted manner. This increases efficiency, reduces costs and strengthens the sustainability of the fleet.
Innovation for maintenance in Europe
Technology and innovation make it possible to carry out maintenance measures precisely when they are required. DB Cargo relies on "SherLok", a fully integrated application for data processing with a web interface for end users, which brings together all relevant data from the entire locomotive fleet:
- Vehicle status data such as engine power, temperatures, consumption and diagnostic messages
- Location information and route histories of the locomotives
- Workshop data including historic, current and planned maintenance measures
Next to automatic monitoring, "SherLok" enables real-time remote diagnostics through self-service for non-technical users. Where data queries once required IT specialists and data scientists, authorized colleagues can now access information directly via a phone or tablet. Specialist expertise can be integrated across all locations - for fast, well-informed decisions and efficient operational support.
SherLOK - Diagnostics at a glance
Automation in maintenance
Automated condition monitoring analyses data for numerous use cases based on algorithms. If a need for action is detected, the necessary maintenance measures are triggered directly in the system. Work orders are created fully automatically - without any delay or manual intervention.
Optimal preparation of maintenance measures
Thanks to "SherLok", information can be provided in an easy-to-use web interface allowing workshops and mobile teams to be fully informed even before a locomotive arrives. Troubleshooting and diagnostics can be done remotely, necessary work can be planned in advance, and materials can be prepared ahead of time. This speeds up processes and significantly reduces downtimes.
This is made possible with a 360-degree view on our rolling stock including real-time geolocation, comprehensive diagnostics and time-series data. Components are not only checked when the locomotive is at a standstill, but also during operation - under full load on the track when an issue actually occurres – which is often impossible to replicate in the workshop. This makes maintenance even more precise and efficient.
Conclusion
By leveraging „SherLok“, DB Cargo transforms reactive maintenance into a proactive, data-driven business process. Real-time insights and automation not only boost availability and reduce costs but also demonstrate how intelligent data use can drive efficiency, sustainability, and smarter decision-making across modern rail operations.
SherLOK - Engine data
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