Maintenance Munich: Leading Trade Fair for Industrial Maintenance

Munich / October 14, 2026 - October 15, 2026

Maintenance 2026

Fair for Industrial Maintenance

We'll be on site at the joint Fraunhofer booth of the Fraunhofer Society's Smart Maintenance Community: Hall 3, Booth 406

Maintenance in Munich is the leading fair for industrial maintenance, where experts from various industries gather annually to present and discuss innovations and trends.

Together with the Fraunhofer Institute for Material Flow and Logistics IML, we are showcasing innovative Smart Maintenance solutions. At our joint booth, you can explore data-driven systems designed to improve Overall Equipment Effectiveness (OEE). Visit us to learn more about predictive maintenance strategies and cutting-edge technologies!

Our Contribution

Our expert, Dr. Benjamin Adrian, will be on site at the Fraunhofer Smart Maintenance Community booth, presenting the institute’s data-driven solutions and software systems designed to sustainably improve Overall Equipment Effectiveness (OEE) in manufacturing:

October 14–15, 2026 | MOC – Event Center Messe München

How can maintenance activities be planned strategically, equipment failures prevented, and technical systems monitored reliably? The Fraunhofer Smart Maintenance Community (SMC) will present answers to these questions.
The focus of our exhibition presence is on data-driven methods and innovative sensor technology for intelligent, condition-based maintenance.
Predictive Maintenance with RUL Models
We demonstrate how the remaining useful life of individual components can be estimated using RUL (Remaining Useful Life) models. This enables maintenance activities to be planned according to actual needs, reduces unplanned downtime, and helps use resources more efficiently.
Contactless Torque Monitoring of Rotating Drive Shafts
Another key focus is the contactless measurement of torsion in rotating drive shafts. The sensor technology developed enables reliable monitoring of drive systems and provides valuable insights for condition-based maintenance.

© Fraunhofer ITWM
Sensor. Non-contact torque measurement

October 15, 2026, 11:00–11:30 a.m. | SolutionCenter, Hall 2

Dr. Benjamin Adrian will give a presentation on the topic »The Mathematics of Maintenance – What Does a Maintenance Measure Actually Achieve?«

Would you like to address a specific challenge in your maintenance operations? Visit our booth to learn more about the Smart Maintenance Matching Day hosted by the SMC. This format gives companies the opportunity to work together with Fraunhofer experts to structure individual challenges and develop concrete solutions.

Video of the presentation: Artificial intelligence meets the test bench – How to create a reliable »RUL forecast« [only available in German]

Our expert Dr. Benjamin Adrian provides insights into the methodology and possibilities of predictive maintenance in his presentation »Artificial intelligence meets the test bench – How to create a reliable RUL forecast.« The presentation took place at the »Maintenance« fair in Munich.

Our Topics

Predictive Maintenance

Industry 4.0 and digitalization increase production efficiency through predictive maintenance and condition monitoring, which minimizes downtime and optimizes maintenance. Our solutions use intelligent algorithms and multi-sensor technology for condition monitoring, automated KPI collection, and effective maintenance strategies, thereby saving costs.

Project »EMILIE«

In the »EMILIE« project – Embedding Machine Intelligence Logic and IT Security into Edge Devices – we are working together with our partners to improve the electronics used for this purpose and the so-called edge gateways. These are designed to securely and robustly collect information, process it based on artificial intelligence (AI), and optimally control and monitor production processes.

AI Applications for Industries with Limited Data

Artificial intelligence is revolutionizing industrial quality control with flexible systems. Despite the challenge of generating sufficient error patterns, we are successfully implementing innovative approaches such as synthetic data and transfer learning. Our projects demonstrate the practical application of AI in quality control.

Inline Quality Control for Production

We offer customized solutions for automated quality control in production that combine classic image processing and machine learning.

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