ESP32 TensorFlow Lite Anomaly Detector Kit — Edge AI for Predictive Maintenance

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

Industrial motors, pumps, and conveyors rely on predictable vibration patterns. When these patterns deviate unexpectedly, early detection can prevent costly breakdowns. This kit teaches you to deploy a real-time anomaly detection system on an ESP32 microcontroller, training a TensorFlow Lite model to classify normal vs. abnormal vibration and sending instant MQTT alerts — a skill directly applicable to Industry 4.0 and IoT careers.

What You'll Build

A self-contained device that continuously reads vibration data from an ADXL345 accelerometer, logs timestamped records to a MicroSD card via the DS3231 RTC, runs a pre-trained TFLite anomaly model on the ESP32, and flashes the OLED display with status while publishing alert messages to any MQTT broker when deviations occur. You'll assemble all hardware and configure the full edge AI pipeline.

What You'll Learn

• Collect and preprocess accelerometer data for time-series analysis
• Train a TensorFlow Lite model for anomaly detection on vibration signatures
• Deploy and optimize a machine learning model on an ESP32 microcontroller
• Implement MQTT-based alerting and timestamped data logging with RTC and MicroSD

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately