Скачать или смотреть TinyML on ESP32 for Vibration Based DC Fan Anomaly Detection

This video is a project showcase for the Machine Learning and Deep Learning course, supervised by Dr. Eng. Handri Santoso.

The project aims to develop machine learning models—Decision Tree, Random Forest, and Neural Network—to classify the condition of an electric fan based on vibration data. The vibration signals are collected using an MPU6050 sensor and an ESP32 microcontroller. The signals are transformed from the time domain to the frequency domain using Fast Fourier Transform (FFT). From the frequency-domain representation, three features are extracted: maximum amplitude, dominant frequency, and mean energy. The trained models are then deployed directly on the ESP32, enabling real-time embedded machine learning implementation.