Electrically induced bearing damage—especially fluting caused by EDM discharges and other bearing‑current phenomena in inverter‑fed drives—has become a significant reliability challenge in modern electric drivetrains. Continuous, early detection of this damage using vibration and bearing‑voltage measurements is essential to avoid unplanned downtime and extend bearing lifetime. This thesis presents a compact, standalone embedded system designed to monitor both signal types in real time and provide a live assessment of bearing health.

The primary goal of this project is to design and develop a single-box real time bearing health monitoring system that acquires vibration and bearing voltage signals, extracts relevant diagnostic features, detects early bearing fluting and EDM discharges, classifies bearing condition as Normal, Alert, or Damage, and provides local data visualization and alarm outputs for reliable condition monitoring of VFD-driven machinery.

To achieve the project target, the following tasks will be carried out:

• To build a Raspberry Pi–based control system.
• To acquire vibration and bearing voltage signals using available DAQ systems.
• To develop a user interface for system configuration.
• To extract diagnostic features from both vibration signals and bearing voltage.
• Validate the system on the existing bearing test rig (real motor).

The student must:

• Have hands-on experience with Raspberry Pi, GPIO, Linux, and Python.
• Have completed at least one course or practical lab (Praktikum) in electric motors or electrical drives.
• Have basic knowledge of sensors, data acquisition, and signal processing.
• Be interested in hardware–software integration and embedded system development.

Interested students are invited to send their CV and transcript of records.