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The University of Bremen is working with industry partners on the MasterKI research project to develop an intelligent status monitoring system for mobile machines. A modular edge solution is being developed that uses an AI-supported cloud platform to monitor the machines' status.
Mobile machines such as harvesters in agriculture or straddle carriers in ports are exposed to high loads. Existing monitoring solutions are often expensive and provide only limited data. MasterKI relies on edge computing combined with AI to enable flexible and scalable real-time status monitoring. “The challenge lies in developing a robust and scalable system that can be flexibly adapted to different operating conditions. The combination of edge computing and AI enables real-time condition monitoring and data-based optimization,” explains Professor Karl-Ludwig Krieger, engineer and principal investigator at the University of Bremen.
A central component of the project is the development of a cloud-based platform that enables demand-driven signal preprocessing, condition monitoring, and data transformation. This helps to close the gap between large amounts of data from test bench environments and real-world field data. “By using transfer models and machine learning, known transmission characteristics can be transferred to new applications. This reduces dependence on extensive field data and makes the system economically attractive,” says Julia Scholtyssek, a research assistant at the ITEM research institute at the University of Bremen.
The project, funded by the Federal Ministry for Economic Affairs and Energy (BMWK), combines the expertise of ANEDO GmbH, SEGNO Industrie Automation GmbH, and the ITEM research institute at the University of Bremen.
The aim is to enable reliable and economical monitoring of drive units in mobile machines through an integrated edge measurement system, a cloud-based analysis platform, and an AI building block system. “Drive systems in mobile machines are exposed to enormous stresses, and wear and tear can lead to costly breakdowns. Our system is designed to detect damage at an early stage through intelligent, self-learning analysis,” comments Matthias Terhaag, project lead at ANEDO.
As mobile machines are often used in safety-critical areas such as ports, data security plays a crucial role. The solutions developed in the project therefore rely on modern encryption technologies to prevent unauthorized access. At the same time, a user-friendly app is being developed that enables intuitive control and monitoring of the systems. “Our solution increases machine availability, reduces operating costs, and contributes to digitalization in industry,” emphasizes Vasco de Freitas, head of sales at SEGNO.
Prof. Dr.-Ing. Karl-Ludwig Krieger, University of Bremen, Faculty 01: Physics / Electrical Engineering, ITEM – Angewandte Elektronik- und Softwaresysteme, Phone: +49 421 218-62550, Email: krieger@uni-bremen.de
Electronics modules of the edge measurement system
Quelle: Timo Lutz Werbefotografie
Copyright: ANEDO GmbH
Merkmale dieser Pressemitteilung:
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Elektrotechnik, Informationstechnik
überregional
Forschungsprojekte, Kooperationen
Englisch
Electronics modules of the edge measurement system
Quelle: Timo Lutz Werbefotografie
Copyright: ANEDO GmbH
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