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AI Case Study

Deutsche Bahn reduces maintenance cost by 25% and delay-causing failures using machine learning

Deutsche Bahn leverages smart sensor and advanced machine learning analytics from Konux to reduce maintenance costs and avoid infrastructure failure. With predictive maintenance, the rail network company has achieved a cost reduction of 25%, through minimisation of downtime and maximisation of performance.



Freight And Logistics

Project Overview

"The German operator has adapted the KONUX solution to replace manual measurements with a position measurement system based on custom-made MEMS (micro-electro-mechanical) sensor clusters. This enables autonomous and continuous monitoring with wireless data transmission. The data is pre-processed in the sensors, and machine learning algorithms in the cloud detect critical wear.

Through real-time monitoring, the health of all critical points can be tracked in real-time via the backend KONUX software. This shift towards predictive maintenance enables a radically enhanced understanding of critical components and need-based maintenance."

Reported Results

The benefits include a cost reduction of 25%, achieved by minimizing downtime and maximizing performance.


"KONUX offers an industrial IoT solution combining smart sensors, data fusion and AI-based analytics to increase asset availability and optimize maintenance planning."



Network Operations


"With infrastructure constantly growing – as well as ageing – condition monitoring of assets is essential to drive efficiency savings (maintenance optimisation), and to reduce the risk of infrastructure failure in the long term. As renewals and maintenance account for almost half of infrastructure managers’ expenditure, smart maintenance planning and spending has become paramount.

A recent bridge collapse in the UK highlighted the issue, when all direct services between London St Pancras and all stations north of Leicester, including the major cities of Sheffield and Nottingham, had to be put on hold. The collapse resulted in severe disruption, and major issues for operators – not to mention headache for thousands of passengers."



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