Collaboration on a project aimed at the development of an advanced diagnostic system for rail vehicles, which will enable predictive diagnostics of the technical condition of selected components and thus enable the replacement of conventional preventive maintenance with predetermined intervals. The project is implemented in cooperation with Škoda Digital and the University of Mining — Technical University of Ostrava. The project is co-financed by the European Union.
Collaboration on a project aimed at the development of an advanced diagnostic system for rail vehicles, which will enable predictive diagnostics of the technical condition of selected components and thus enable the replacement of conventional preventive maintenance with predetermined intervals. The project is implemented in cooperation with Škoda Digital and the University of Mining — Technical University of Ostrava. The project is co-financed by the European Union.
transition to predictive maintenance
optimization of operating costs
research scope for machine learning
Škoda Digital has identified the need to upgrade its PREMIS diagnostic system with predictive diagnostics to enable a shift from preventive maintenance to predictive maintenance, optimizing costs and increasing the efficiency of rail vehicle operations. For Favorlogic, this was an ideal opportunity to leverage its experience in developing cloud solutions and to gain new knowledge and experience in the field of machine learning and predictive modeling.
analysis of operational trends
predictive models of technical condition
integration of direct and indirect measurements
scalable microservice architecture
We participate in the research and development of a comprehensive predictive diagnostics system. The system uses modern machine learning approaches to analyze trends in the technical condition of rail vehicle components. Development includes integration of direct and indirect measurements, data visualization, updatable predictive models, and a scalable microservice-based architecture.
project in an early stage of implementation
data-driven maintenance planning
foundation for optimizing service intervals
The project is in the early stages of implementation. The resulting system is expected to enable efficient planning of rail vehicles maintenance based on current operational data and prediction of component technical condition.
