Humaware (EKE-Electronics) Case Studies Cooperation with VR FleetCare for predictive analytics

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	Cooperation with VR FleetCare for predictive analytics Humaware (EKE-Electronics)

Cooperation with VR FleetCare for predictive analytics

Humaware (EKE-Electronics)

Technology Category	Analytics & Modeling - Edge Analytics Analytics & Modeling - Predictive Analytics Functional Applications - Remote Monitoring & Control Systems Infrastructure as a Service (IaaS) - Cloud Computing Infrastructure as a Service (IaaS) Networks & Connectivity - Gateways
Applicable Industries	Railway & Metro
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance
Challenge	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. Read More
About Customer	VR FLEETCAREA 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 tech Read More
Customer Name	VR FleetCare Read More
Solution	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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Edge Analytics

Analytics & Modeling - Predictive Analytics

Functional Applications - Remote Monitoring & Control Systems

Infrastructure as a Service (IaaS) - Cloud Computing

Infrastructure as a Service (IaaS)

Networks & Connectivity - Gateways

Applicable Industries

Railway & Metro

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

Challenge

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.

About Customer

VR FLEETCAREA 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 tech

Customer Name

VR FleetCare

Solution

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.

Impact #1	[Cost Reduction - Maintenance] Our aim is to generate savings of approximately 10–15% with this new method

Impact #1

[Cost Reduction - Maintenance]

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

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Overview

Cooperation with VR FleetCare for predictive analytics

Operational Impact