Maintenance: Leading trade fair for industrial maintenance

Dortmund / February 25, 2026 - February 26, 2026

Maintenance 2026

Leading trade fair for industrial maintenance

We are on site at the Fraunhofer Smart Maintenance Community joint booth: Hall 4 Booth number: 4-D01

Maintenance in Dortmund is the leading trade fair for industrial maintenance, where experts from various industries meet annually to present and exchange innovations and trends.

Together with the Fraunhofer Institute for Material Flow and Logistics IML, we present innovative solutions for smart maintenance. Our joint booth offers insights into data-based systems that improve Overall Equipment Effectiveness (OEE). Visit us to learn more about predictive maintenance strategies and modern technologies!

Our colleague Dr. Benjamin Adrian is on site at the Fraunhofer Smart Maintenance Community booth, presenting the institute's data-based solutions and software systems that sustainably improve Overall Equipment Effectiveness (OEE) in manufacturing:

Monitoring drive shafts: Using the magnetostrictive sensor, we demonstrate how torsion and rotational speed can be integrated into condition monitoring in typical drive trains, even retrospectively. The software enables dynamic and static torsional loads to be detected.
Integrated quality monitoring: Using imaging techniques, we demonstrate how quality control can be integrated into condition monitoring in order to detect quality defects at an early stage and thus reduce avoidable scrap.
Predictive maintenance: We present how to build AI models step by step to use mathematical methods to record the wear and tear of machines and predict it, taking utilization into account.

Demonstrator VibDemo — © Fraunhofer ITWM
Wir demonstrieren diese Lösungen am Beispiel eines miniaturisierten Antriebstrangs VibDemo, um interaktiv darzustellen, wie sich Maschinenzustände in interschiedlichen Lastszenarien verändern und trotzdem überwacht werden können.

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.

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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.

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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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Fair

Dortmund / February 25, 2026 - February 26, 2026

Maintenance 2026

Mehr Informationen

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

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Our Topics

Predictive Maintenance

Project »EMILIE«

AI Applications for Industries with Limited Data

Inline Quality Control for Production

Contact Press / Media

Dr. Benjamin Adrian