How to improve workshop diagnostics with an intelligent self-learning system
Background of the thesis work:
A big success factor for a vehicle workshop is to be able to quickly do analysis and classification of the root cause for a vehicle failure. Today, analysis of faults is made by experienced and educated personnel using diagnostics tools, manuals, and in some cases, a call center of super-technicians.
The idea of this thesis project is to build a decision support system based on an intelligent self-learning feedback system. The system can help the technician to make a better and quicker problem analysis based on previous problems and solutions.
Area of thesis research
- Conduct a literature study on different approaches to create an intelligent self-learning system
- Conduct interviews with workshop mechanics to understand how the process from detecting a fault to finding the root cause looks like today
- Define possible architectures and best practices for how to build a digital mechanic system
- Make a POC that demonstrates the ability of the system
- Write a report, suggest recommendations for a suitable self-learning architecture, and identify topics for future research
Suitable background of students:
This thesis is suited for students with a Master of Science in Computer Science or a similar education. You will possess good communication skills in English, shows a high level of initiative, and is self-driven.
Application deadline: 10:th of December
Suggested start: Jan 2023
Number of students: Preferably the thesis is done by at least two persons