Remote diagnosis and Deep Learning at Zentralbahn – added value thanks to data and expert know-how
Zentralbahn, based in Stansstad (NW), has implemented several digitalization projects in its fleet. One project involves remote diagnostics of the ABe fleet (SPATZ) with Rail Diagnostics. In another project, in collaboration with CSEM, existing data from the ABe fleet (ADLER, FINK) is enriched with vibration data and other sensor data and evaluated using deep learning algorithms.
This article was first published in the October 2024 issue of the trade journal “Eisenbahntechnische Rundschau” (ETR). The summary below, provided by CSEM, presents the key milestones, while the full report can be downloaded as a PDF document in German, courtesy of the publisher.

Summary
The Zentralbahn is advancing the digital transformation of its railway operations by implementing remote diagnostics and deep learning technologies. A key initiative is the real-time remote diagnostics system for the ABe fleet (SPATZ), leveraging cutting-edge analytics from Rail Diagnostics to monitor and assess operational data. Simultaneously, the existing data from the ABeh fleet (ADLER, FINK) is enriched with vibration and other sensor inputs, which are then analyzed using AI-powered algorithms. These innovations enable condition-based maintenance, reducing downtime and enhancing fleet efficiency.
As part of the deep learning project, a specialized IoT platform collects thousands of sensor signals in near real-time. Neural networks process this data to detect anomalies early and prevent potential malfunctions. By leveraging artificial intelligence, the system not only improves predictive maintenance accuracy but also drives long-term cost savings. Additionally, an advanced remote diagnostics system ensures continuous fleet monitoring, supports maintenance planning, and assists personnel in real time—leading to increased operational safety and reliability.
These pioneering initiatives underscore Zentralbahn’s commitment to data-driven maintenance strategies. By partnering with Rail Diagnostics and CSEM, the company continuously enhances and refines these technologies. Through AI-driven predictive analytics, Zentralbahn positions itself at the forefront of digital transformation in rail transport, contributing significantly to efficiency improvements and operational excellence in the industry.
Authors
- Marco Barmettler, Project Manager IT Applications Vehicles and Maintenance, Stansstad, zb Zentralbahn AG
- Dr. Jihyun Lee, R&D Engineer, Group Predictive Analytics, Alpnach, CSEM
- Florian Burri, Project Manager, Group Industrial Inspection, Alpnach, CSEM
- Roman Tschannen, Managing Director, Dietikon, Rail Diagnostics GmbH
- Severin Wagner Director of Engineering / Deputy Managing Director, Dietikon, Rail Diagnostics GmbH