ESP32 ML Anomaly on Power Signature Kit with ESP32 + LED
ESP32 ML Anomaly Detection Kit — Build a Predictive Maintenance System with Edge Impulse and TFLite
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
Transform a standard appliance into a self-monitoring machine that learns its own electrical heartbeat. Using the ACS712 current sensor and an ESP32 running a TensorFlow Lite model trained on Edge Impulse, you’ll build a device that captures power signatures, trains a neural network to recognize normal behavior, and instantly flags anomalies indicating faults like motor wear, arcing, or capacitor degradation. This kit puts industrial-grade anomaly detection directly into your hands.
What You'll Build
You'll assemble a compact DIN-rail enclosure housing an ESP32-based monitor that logs power waveforms to microSD, timestamps them with a DS3231 RTC, and displays real-time status on an OLED. Once trained on your chosen appliance’s signature, it alerts you when current patterns deviate beyond a learned threshold — perfect for monitoring pumps, compressors, or any motor-driven equipment.
What You'll Learn
- Capture and preprocess AC current waveform data using the ACS712 and ESP32 ADC sampling
- Train a machine learning model on Edge Impulse to classify normal vs. anomalous power signatures
- Deploy a TensorFlow Lite model onto the ESP32 for real-time inference
- Design a standalone IoT fault detection system with data logging and timestamped storage
Kit Contents
| Component | Quantity |
|---|---|
| ESP32 Dev Board | 1 |
| ACS712 20A | 1 |
| DS3231 RTC | 1 |
| MicroSD Module | 1 |
| 0.96in OLED | 1 |
| LM2596 Buck Converter | 1 |
| 100nF Caps | 10 |
| PCB Prototype Board | 2 |
| DIN Rail Enclosure | 1 |
| 12V 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
Designed for final-year B.Tech ECE/EEE students working on IoT and ML projects, Smart India Hackathon teams needing an edge AI solution, and ATL Tinkering Lab mentors introducing predictive maintenance concepts. If you’re at IIT, NIT, VIT, or BITS pursuing a major project, this kit accelerates your prototype from idea to demo-ready in one weekend.
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?
Our AI companion, accessible via QR code, walks you through every step — from soldering to model training. You can also message us on WhatsApp for real-time troubleshooting.
Can I train the model on any appliance, like a fan or water pump?
Yes, the kit works with any single-phase AC appliance up to 20A. The Edge Impulse workflow is appliance-agnostic; you collect normal data, label it, and the platform generates a model. Deploy the resulting TFLite file to the ESP32 in minutes.
Do I need prior ML experience to use this kit?
No. The kit includes a guided notebook and our AI companion that simplifies Edge Impulse setup. Basic familiarity with Arduino IDE is helpful but not mandatory — we provide ready-to-flash firmware for data collection and inference.
Is this kit suitable for the Smart India Hackathon hardware edition?
Absolutely. It aligns perfectly with themes like predictive maintenance, energy monitoring, and smart appliances. The compact DIN-rail form factor and ML integration make it a standout prototype for judges.
ACS712 current waveform fingerprint trained with Edge Impulse. ESP32 detects appliance faults via TFLite.
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