{"product_id":"kit-edge-ai-anomaly-detection-node","title":"Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050","description":"\u003ch1\u003eBuild an Edge AI Anomaly Detection Node with ESP32 and MPU6050 for Predictive Maintenance\u003c\/h1\u003e\n\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 15-20 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 25+\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Edge AI Model Deployment and Sensor Fusion\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eEquip 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou 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.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eDeploy TensorFlow Lite Micro on ESP32 and optimize a neural network for an 8-bit MCU\u003c\/li\u003e\n  \u003cli\u003eMulti-sensor data acquisition and windowed preprocessing (accelerometer, temperature, current)\u003c\/li\u003e\n  \u003cli\u003eAutoencoder-based anomaly detection: training, quantising, and converting a model for on-device inference\u003c\/li\u003e\n  \u003cli\u003eIntegrate real-time inference results with an OLED display and external alert logic\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eESP32 Dev Board\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMPU6050\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDHT22\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eACS712 5A\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e0.96in OLED\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eLM2596 Buck Converter\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e100nF Caps\u003c\/td\u003e\n\u003ctd\u003e10\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e4.7kΩ Resistors\u003c\/td\u003e\n\u003ctd\u003e5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePCB Prototype Board\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eEnclosure Box\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e5V 2A PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSoldering Iron\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eSolder Wire\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eAdvanced 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\u0026amp;D engineers in predictive maintenance, IoT developers deploying condition-monitoring at scale, and tinkerers who want to push beyond cloud-dependent machine learning.\u003c\/p\u003e\n\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eScan 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.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I modify the TensorFlow Lite model to detect different types of anomalies?\u003c\/summary\u003e\u003cp\u003eAbsolutely. 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.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior experience with machine learning to build this kit?\u003c\/summary\u003e\u003cp\u003eFamiliarity 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.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eHow does the edge AI differentiate this from a simple threshold-based sensor alarm?\u003c\/summary\u003e\u003cp\u003eInstead 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.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eESP32 runs TensorFlow Lite anomaly detection model trained on sensor time series. Flags anomalies locally without cloud.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/esp32-30-pin-development-board-cp2102-wifi-bluetooth\"\u003eESP32 Dev Board\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/mpu6050-imu-module-6-axis-gyro-accelerometer-for-arduino\"\u003eMPU6050\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/dht22-temperature-humidity-sensor-module-accurate-readings\"\u003eDHT22\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/acs712-5a-current-sensor-module-precise-hall-effect-measurement\"\u003eACS712 5A\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/096in-oled-display-128x64-i2cspi-for-arduino-raspberry-pi\"\u003e0.96in OLED\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/lm2596-buck-converter-step-down-voltage-regulator-module\"\u003eLM2596 Buck Converter\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/capacitor-variety-pack-6-values-100nf-to-470uf-30-pieces\"\u003e100nF Caps\u003c\/a\u003e x10\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/resistor-variety-pack-100-pcs-10-values-14w-carbon-film\"\u003e4.7kΩ Resistors\u003c\/a\u003e x5\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/esp-wroom-32-breakout-board-pcb-55x52mm\"\u003ePCB Prototype Board\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/enclosure-box-for-diy-electronics-projects-compoden\"\u003eEnclosure Box\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/4-channel-relay-board-for-esp32-30-pin-5v-control\"\u003e5V 2A PSU\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/soldering-kit-25w-with-solder-wire-flux-paste-compoden\"\u003eSoldering Iron\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/soldering-kit-25w-with-solder-wire-flux-paste-compoden\"\u003eSolder Wire\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050 includes all components needed: ESP32 Dev Board, MPU6050, DHT22, ACS712 5A, 0.96in OLED and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Expert level makers, suitable for ages 25+. ESP32 runs TensorFlow Lite anomaly detection model trained on sensor time series. Flags anomalies locally without cloud. Estimated build time is 15-20 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050 online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050 is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Edge AI Anomaly Detection Node Kit with ESP32 + MPU6050\",\n  \"description\": \"ESP32 runs TensorFlow Lite anomaly detection model trained on sensor time series. Flags anomalies locally without cloud.\",\n  \"sku\": \"CDN-KIT-0535\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-edge-ai-anomaly-detection-node\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"4660\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"IoT \u0026 Connectivity\"\n}\n\u003c\/script\u003e\u003cp\u003e\u003cstrong\u003eChoose your assembly option:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoldering Kit\u003c\/strong\u003e — 25W soldering iron, 60\/40 solder wire, flux, and small perfboard for permanent assembly.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eBreadboard Combo\u003c\/strong\u003e — 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Compoden","offers":[{"title":"Soldering Kit","offer_id":53459794493805,"sku":"CDN-KIT-0535-SLD","price":3930.0,"currency_code":"INR","in_stock":true},{"title":"Breadboard Combo","offer_id":53459794526573,"sku":"CDN-KIT-0535-BB","price":3330.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-edge-ai-anomaly-detection-node.png?v=1781944529","url":"https:\/\/compoden.com\/products\/kit-edge-ai-anomaly-detection-node","provider":"Compoden","version":"1.0","type":"link"}