Predictive Maintenance has become a crucial topic in the industry, enabling early detection of potential ma- chine failures through changing measurements. Vibra- tions, current, and temperature are key metrics ana- lyzed to prevent downtime. Despite its importance, understanding how faults influence measurements re- mains challenging, often requiring detailed knowledge of machine structures and components. This thesis ex- plores the potential of AI to enhance damage detec- tion and localization, leveraging innovative techniques to improve industrial maintenance processes.
Download as PDF
Student Target Groups:
- Computer Science (CS)
- Information and Computer Engineering (ICE)
- Electrical Engineering (EE)
Thesis Type:
- Master Thesis / Master’s Project
Goal and Tasks:
- Investigate existing AI methods for Predictive Main- tenance (e.g., Classification, Anomaly Detection).
- Compare MCU platforms for AI implementation.
- Develop a Predictive Maintenance system using the X20AI4632 module and Renesas R7FA4M2AB3CNE.
- Evaluate system performance and reliability on real-world hardware setups.
Recommended Prior Knowledge:
- Basics of Machine Learning and AI
- Embedded Systems and Microcontrollers
- Signal Processing and Analysis
- Programming Skills (e.g., Python, C/C++)
Start:
Contact: