![[Translate to Englisch:] ZF Friedrichshafen AG - doubleSlash](/fileadmin/_processed_/e/8/csm_Snippet_Referenzen_ZF-Scrum_Facebook_7e9e27e5b8.jpg "[Translate to Englisch:] ZF Friedrichshafen AG - doubleSlash")
Predictive maintenance for railway vehicles
Actively preventing damage to wheels and rails with IoT cloud concepts
ZF Friedrichshafen AG is a leading global technology group in driveline and chassis technology. They rely on state-of-the-art cloud-based predictive maintenance methods for early detection of damage to wheels and rails on railway vehicles. This minimises the effort for rail technicians as well as increasing the repair efficiency. Sources of noise for local residents are also eliminated. This is the basis for providing the best possible experience for passengers and citizens.
+49 7541 70078-767Keeping an eye on wheel and rail wear with intelligent maintenance
Operation of rail-bound transport systems depends to a large extent on perfect rolling contact between the steel wheel and steel rail. Damage or wear on either side can lead to vibrations, wear and possible danger spots. This can result in increased maintenance costs or noise pollution for residents in the local vicinity.
The goal of railway or tramway operators is therefore to keep noise emissions and maintenance costs as low as possible. To address this issue, ZF Friedrichshafen AG developed a digitalisation solution for comprehensive condition monitoring of wheel tyres and rail infrastructure. doubleSlash supported the project with the conception of a cloud platform to import, analyse and evaluate the collected vehicle data autonomously using Microsoft Azure Cloud.
The application can also be used to monitor whole networks of fleets and trains. This will hopefully pave the way for more efficient, eco-friendly planning of vehicle availability in the urban transport sector.
“We’re delighted to have reached a first milestone in terms of automated condition monitoring and predictive maintenance planning with the ‘wheel flat spot detection’ function. As our technology partner, doubleSlash provided optimal support for this project. We’ve now paved the way for more efficient, eco-friendly planning of vehicle availability in the urban transport sector in future.”
Alan Dittrich // Digital Solutions Manager for Rail Vehicle Driveline Technology // ZF Friedrichshafen AG
After it has been automatically collected and sent to ZF’s cloud-based IoT platform, the data is evaluated and appropriate maintenance notifications are created.
Picture 1: ZF’s IoT connection
Rail damage has a lasting effect on the driving activity of a railway vehicle. Searching for the damage is usually time-consuming. The flat spots shown in the picture cause damage to the rails and generate a disturbing noise level. If detected at an early stage, they can be repaired quickly.
Picture 2: Flat spot
The data is processed by the Microsoft Azure Cloud backend developed by doubleSlash. The frontend developed using Angular and TypeScript prepares this data for the user, and the evaluated IoT data from the cloud is presented on the platform.
Picture 3: Image of the digitalisation solution, November 2020
An innovative and intelligent web application thanks to many years’ experience in IoT and Microsoft Azure
As agile software and IT partner, doubleSlash assisted ZF in implementing the project with comprehensive consulting services and technological know-how: right from product vision to project conception and management. doubleSlash also supported backend and frontend development. Using Scrum methodology, a functioning product was developed in a series of short iterative cycles. The range of services also includes support and bug fixing for the platform.
Our services:
- Agile project management with Scrum
- IoT architecture
- Agile software development
- IoT backend development
- Frontend development
- Design and functionality
- Testing
Technologies used:
- Azure (Key Vault, Azure AD, Event Grid, Application Insights, Function App, Cosmos DB, Logic App, Redis Cache, Blob Storage)
Python
.Net
- Angular
Terraform
WordPress (User Manual)
