Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050
Build an Edge AI Anomaly Detection Node with ESP32 and MPU6050 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.
Equip a motor, pump, or industrial spindle with on-device intelligence that flags abnormal behavior the moment it occurs. This kit guides you through training a TensorFlow Lite model on real vibration, temperature, and current signatures, then deploying it onto an ESP32 so the node can detect anomalies locally—no cloud, no latency, no recurring subscription. It’s the same approach used in smart factories to cut downtime, now accessible as a project you build, program, and mount inside a ready enclosure.
What You'll Build
You will assemble a self-contained edge computing node that reads MPU6050 3-axis motion, DHT22 ambient temperature/humidity, and ACS712 current draw. A TensorFlow Lite Micro model runs inference on that fused data stream in real time, lighting the OLED green under normal operation and red when an anomaly is detected. The fully enclosed device can be clamped onto any machine and left to monitor round the clock.
What You'll Learn
- Deploy TensorFlow Lite Micro on ESP32 and optimize a neural network for an 8-bit MCU
- Multi-sensor data acquisition and windowed preprocessing (accelerometer, temperature, current)
- Autoencoder-based anomaly detection: training, quantising, and converting a model for on-device inference
- Integrate real-time inference results with an OLED display and external alert logic
Kit Contents
| Component | Quantity |
|---|---|
| ESP32 Dev Board | 1 |
| MPU6050 | 1 |
| DHT22 | 1 |
| ACS712 5A | 1 |
| 0.96in OLED | 1 |
| LM2596 Buck Converter | 1 |
| 100nF Caps | 10 |
| 4.7kΩ Resistors | 5 |
| PCB Prototype Board | 2 |
| Enclosure Box | 1 |
| 5V 2A PSU | 1 |
| Soldering Iron | 1 |
| Solder Wire | 1 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify every part | Pre-tested as a system |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project |
| Time to first working build | Days of debugging | Hours, with step-by-step guidance |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
Advanced B.Tech and M.Tech students in ECE, EEE, or AI from IITs, NITs, VIT, BITS Pilani, and similar institutions will find this kit ideal for Smart India Hackathon projects, final-year Industry 4.0 prototypes, and research papers on edge AI. It also serves R&D engineers in predictive maintenance, IoT developers deploying condition-monitoring at scale, and tinkerers who want to push beyond cloud-dependent machine learning.
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.
What if I get stuck during the build?
Scan the QR code to open the AI companion trained on this specific project; it walks you through wiring, code upload, and debugging. For anything beyond its scope, our team replies on WhatsApp within hours.
Can I modify the TensorFlow Lite model to detect different types of anomalies?
Absolutely. The companion guide covers how to capture your own sensor data, retrain the autoencoder, and convert it to a .tflite model. The code is structured so you can adapt the input features and reconstruction threshold.
Do I need prior experience with machine learning to build this kit?
Familiarity with Python and basic ML concepts (training, overfitting) is recommended, but the AI companion provides the complete training notebook and explains every step. Many advanced undergraduate students complete it within the 20-hour window.
How does the edge AI differentiate this from a simple threshold-based sensor alarm?
Instead of rigid limits that trigger false alarms under normal load changes, the neural network learns the machine's entire operating signature. It catches subtle deviations invisible to single-sensor thresholds, giving you predictive insight months before a breakdown.
ESP32 runs TensorFlow Lite anomaly detection model trained on sensor time series. Flags anomalies locally without cloud.
What's in this kit
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.
Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items