Predictive maintenance as a value driver
All over the world, railway infrastructure managers are striving to maintain the highest possible availability of their tracks. When doing so, however, they are confronted with extreme challenges: ageing assets, a dwindling workforce or rising capital and operating costs. One of the most critical assets of the railway infrastructure is the turnout. It is particularly relevant for ensuring network availability, causing around 20 to 30 percent of all infrastructure-related minutes of delay (KONUX Global Rail Market Analysis 2018). It is also one of the assets with the highest maintenance costs per metre. Around the world, they amount to 12 billion euros per year for maintenance and replacement. That is why the operators of infrastructures are eager to gain more meaningful insights that will help them monitor, inspect, maintain and renew turnouts more efficiently and effectively. The challenge is that most of the methods currently in use only provide a mere snapshot of the condition of the assets and only capture their current state. Single data points from just a few passing trains can result in misleading findings and, not least, in assets being over- or under-maintained.
The Predictive Maintenance System for railway turnouts developed by KONUX GmbH from Munich is a software-as-a-service (SaaS) solution that uses IoT units and artificial intelligence to improve network capacity, reliability and cost efficiency. It continuously and autonomously monitors the condition of key turnout components such as the track bed and the frog. The KONUX system provides infrastructure managers with a forecast of how the condition of the turnouts will develop over time, enabling them to prevent failures and optimise their maintenance planning. At the end of 2020, KONUX and Deutsche Bahn concluded the first long-term, cloud-based SaaS framework agreement for the digitalisation of turnouts as critical elements of the railway infrastructure.
Integrated future-oriented approaches
Assessing the value of digitalization requires infrastructure managers to adapt their processes, not just by replacing each manual measurement with a digital equivalent, but also by holistically rethinking the insights required to monitor and maintain the turnout. Ultimately, this means the integration of different approaches, for example measurement trains in combination with permanently installed autonomous IoT units, in order to realise an overall improvement in performance while saving costs, as with the system being outlined here.