TinyML Vibration Fault Classifier Kit: Build an AI-Powered Predictive Maintenance System with ESP32-S3 & MPU6050

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Ever wondered how factories predict machine breakdowns before they happen? With this kit, you’ll train a tiny machine learning model on an ESP32-S3 that reads vibration data from the MPU6050 accelerometer, classifies patterns as normal or faulty, and lights a green or red LED accordingly — just like a real industrial predictive maintenance system.

What You'll Build

You’ll assemble a compact module with the ESP32-S3, MPU6050, and OLED display that continuously monitors vibration. It shows real-time classification on the OLED: ‘Normal Operation’ or ‘Fault Detected’. The LEDs provide instant visual feedback — a green LED blinks when the motor is healthy, and a red LED pulses on a fault.

What You'll Learn

• How to collect and label accelerometer data for machine learning
• Uploading a pre-trained Edge Impulse model to an ESP32-S3 microcontroller
• Reading real-time sensor data from MPU6050 and running on-device inference
• Integrating an OLED display to show classification results and status LEDs for output

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Ideal for CBSE Class 11–12 students exploring AI and IoT, B.Tech ECE/EEE first-years working on mini-projects, and ATL Tinkering Lab participants who want a hands-on introduction to TinyML and predictive maintenance. Also perfect for Smart India Hackathon teams building industrial monitoring prototypes.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.