The increasing adoption of e-bikes has resulted in a growing number of electric motors being produced, used, and eventually discarded. These motors are high-value components, yet they are often poorly repairable. When a motor fails, it is usually replaced rather than repaired, even though more than half of the failures are caused by mechanical components that are theoretically repairable. It is not viable for bike mechanics to open or repair motors themselves, but they play an important role in monitoring and diagnosing problems. At the same time, their ability to assess the internal condition of e-bike motors is limited by closed systems, software-based diagnostics, and a lack of mechanical insight.

This project explores how bike mechanics could be better supported in the transition towards a more circular e-bike motor system. The research combines expert interviews, system analysis, technical teardowns, disassembly mapping, and exploratory testing to understand current barriers to repairability and identify opportunities for improvement. The findings show that mechanical failures, particularly in bearings and gears, occur frequently and are often technically repairable. However, limited ability to detect and diagnose the right signals means that motors are often not, or only at a late stage, referred to revision specialists.

Based on these insights, a non-invasive, vibration-based diagnostic concept was developed. The proposed tool enables bike mechanics to measure vibration signals from the outside of the motor and compare them with baseline data from healthy motors. In doing so, it supports mechanical diagnosis and helps mechanics make more informed decisions about follow-up actions. Prototyping and testing demonstrated a proof of principle that vibration analysis can be used to distinguish between healthy and defective motors.

The project also highlights important limitations of the proposed concept. It depends on the availability of reliable baseline data, a well-developed revision infrastructure, and clear responsibility for software and data management. Rather than presenting a final solution, this project provides a research direction, a technical proof of principle, and a system-level perspective on the role of diagnosis in improving e-bike motor repairability. It offers a foundation for further research and development towards a more circular e-bike motor system.