This repository contains the assignments and projects for the course Advanced Edge Computing (ECE7104) at Inha University.
Industrial Rotating Equipment Anomaly Vibration Detection System. This system uses vibration data from operational rotating machinery to identify equipment failure causes. It classifies the data into three categories: two anomalies and one normal condition.
- Imbalance: Uneven mass distribution in the shaft.
- Misalignment: Misalignment of the shaft.
- Normal: Normal operating condition.
Bluetooth communication enables real-time monitoring and visualization of the equipment's operational status.
We use the Machine Fault Simulator to generate vibrations by rotating a motor. The vibrations are captured using the Arduino Nano 33 BLE Sense equipped with a built-in IMU sensor.
During training, we transmit the collected data to the Edge Impulse project space via the Edge Impulse SDK.
Using Edge Impulse, we manage the collection and preprocessing of data for training. Post-training, the model is exported as an Arduino library for deployment.
With the model exported as a library, we develop an application with Bluetooth functionality. The application is then built, deployed, and uploaded to the target device for seamless integration.
Devices equipped with Bluetooth (We use Android Smartphone) receive and visualize the model's inference results in real-time, enabling immediate monitoring and alerting.