ESP32 Predictive Maintenance Node Kit with ESP32 + LED — Classify Motor Faults at the Edge

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

You'll build a self-contained edge node that reads motor current with the ACS712 and vibration with the ADXL345, then classifies faults—electrical overload, bearing wear, misalignment—using a TensorFlow Lite model on the ESP32. This isn't just blinking LEDs; you'll deploy real-time machine learning to keep machines running, making it a standout project for predictive maintenance hackathons and engineering curriculums.

What You'll Build

A DIN-rail-mounted device that monitors a motor in real time, logs data to microSD with timestamps from the DS3231 RTC, and displays fault predictions on the OLED. It's a ready-to-demonstrate predictive maintenance prototype for IoT projects, Smart India Hackathon challenges, and final-year engineering submissions.

What You'll Learn

• Capture and preprocess synchronous current and vibration data for fault detection
• Train and quantize a TensorFlow Lite model for multi-class fault classification on an ESP32
• Implement data logging with RTC timestamps for time-series analysis
• Package a reliable edge node with power management and noise filtering

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Choose your assembly option:

• Soldering Kit — 25W soldering iron, 60/40 solder wire, flux, and small perfboard for permanent assembly.
• Breadboard Combo — 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.