Location: Solna, Stockholm, Sweden
Start: Spring 2026
Workload: Full-time (30 ECTS)
Language: English
Number of students: 2
Thesis Topic
We are looking for two master's students to explore the use of TinyML for predictive maintenance and fault diagnosis in resource-constrained embedded systems.
The project will start by designing and building a small controllable electromechanical system that can operate normally as well as reproduce different types and levels of faulty behavior. The students will then investigate how sensor data from the system can be used by machine-learning models running directly on a microcontroller to detect and classify faults. The thesis therefore combines two main areas:
Design and implementation of a controllable embedded test systemDevelopment and optimization of TinyML models for fault diagnosisThe final demonstrator should be able to monitor the system, identify abnormal behavior, determine the likely type and severity of the fault, and provide an appropriate maintenance recommendation.
Embedded systems and test platform
Develop the embedded software for real-time system control, fault injection, and synchronized data acquisition.Implement configurable and reproducible fault conditions with different severity levels.Build a labeled dataset covering normal operation and the selected fault conditions.TinyML and fault diagnosis
Investigate suitable machine-learning approaches for fault detection and classification using embedded sensor data.Develop and train models using Python and relevant ML frameworks.Investigate different sensor combinations and their impact on fault-diagnosis performance.Deploy suitable models on a resource-constrained microcontroller.Evaluate the trade-offs between accuracy, memory consumption, computational requirements, latency and energy consumption.Investigate model optimization techniques such as quantization, pruning and, where appropriate, knowledge distillation.Evaluate the resulting models on the physical system under different operating conditions and fault severities.Investigate how detected faults can be mapped to appropriate maintenance actions.
