Cooperation with VR FleetCare for predictive analytics


Cooperation with VR FleetCare for predictive analytics
Humaware Humaware
Contact Vendor
Feature New Record

Bogies are the most significant components of the rail fleet in terms of lifecycle costs and traffic safety. In addition to creating significant cost savings for the rail fleet owners, data-driven maintenance will enhance safety and the usability of the rolling stock. The predictive maintenance capability will improve reliability of the trains, cost-efficiency and passenger comfort. Train traffic will operate more reliably when it is possible to predict rolling stock malfunctions before they cause disruptions in traffic.


A multiple-brand rail equipment service-provider, VR FleetCare is a subsidiary of the Finnish railway operator VR Group. We provide our customers with quality railway traffic equipment repair, maintenance and lifecycle services as well as technical fleet expert services in the Nordic and Baltic countries. We are strongly invested in the future, and our operations are guided by continuous development and cutting-edge technologies. With an annual turnover of approximately EUR 200 million, VR FleetCare employs more than 1,000 employees.

The cooperation is based on VR FleetCare’s technical rail fleet expertise and fleet maintenance optimisation as well as EKE-Electronics’ experience in train automation systems and remote condition monitoring.

The aim is to develop a system that predicts the maintenance need and optimizes the service program of bogies. Data is acquired from the trains by sensors and sensor gateways and a smart combination of cloud and edge computing is applied for the signal analysis. The solution utilises the Adaptive Anomaly Detector and predictive analytics developed by Humaware, EKE’s British subsidiary, integrated into EKE’s cloud based remote monitoring software Smartvision™.

Sensors and sensor gateways will be installed in bogies of VR Group’s locomotives and electric trains during this year. More extensive results of the development work can be expected next year.

Mature (technology has been on the market for > 5 years)

Our aim is to generate savings of approximately 10–15% with this new method

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