Master Thesis Proposal: TinyML for Predictive Maintenance in Embedded Systems

📋 Description

• Develop the embedded software for real-time system control, fault injection, and synchronized data
• Implement configurable and reproducible fault conditions with different severity levels.
• Build a labeled dataset covering normal operation and the selected fault conditions.
• Investigate suitable machine-learning approaches for fault detection and classification using
• Develop and train models using Python and relevant ML frameworks.
• Investigate different sensor combinations and their impact on fault-diagnosis performance.

🎯 Requirements

• Good programming skills in C/C++ and Python.
• Experience working with microcontrollers and embedded systems.
• Familiarity with software development using Git.
• Interest in combining software, hardware and machine learning.
• Ability to work independently and systematically.
• Good analytical and problem-solving skills.