{"product_id":"kit-esp32-s3-tinyml-predictive-maintenance","title":"ESP32-S3 TinyML Predictive Maintenance","description":"\u003ch1\u003eESP32-S3 TinyML Predictive Maintenance — Build an AI-Powered Vibration Anomaly Detector with Edge Impulse\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 Intermediate\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 5-6 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 16-21\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e TinyML Anomaly Detection\u003c\/span\u003e\n\u003c\/div\u003e\n\n\u003cp\u003eTurn vibration patterns into early warnings for rotating machinery. With this kit, you’ll train a neural network on the Edge Impulse platform and deploy it to an ESP32-S3 that monitors a motor in real time, lighting up an alert the instant it detects abnormal vibration—before a failure causes downtime. It’s predictive maintenance you can hold in your hand.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’ll assemble a compact sensor node that listens to vibrations from a fan, pump, or motor. The ESP32-S3 continuously analyzes the accelerometer stream, running a TensorFlow Lite model that classifies normal operation vs. anomalous patterns. When a fault signature appears, the red LED and piezo buzzer trigger an immediate alert, creating a complete edge AI alerting system.\u003c\/p\u003e\n\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eTrain an anomaly detection model in Edge Impulse using real vibration data from the ADXL345\u003c\/li\u003e\n  \u003cli\u003eDeploy a TensorFlow Lite micro model onto the ESP32-S3 for on-device inference\u003c\/li\u003e\n  \u003cli\u003eInterface the ADXL345 over I2C and process raw vibration signals into meaningful features\u003c\/li\u003e\n  \u003cli\u003eBuild a full TinyML pipeline from data collection and model training to real-time hardware alerting\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-S3 Dev Board\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eADXL345 Accelerometer\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eLED Red\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eLED Green\u003c\/td\u003e\n\u003ctd\u003e2\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePiezo Buzzer\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e220Ω Resistors\u003c\/td\u003e\n\u003ctd\u003e5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMicroUSB Cable\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-M Wires\u003c\/td\u003e\n\u003ctd\u003e15\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\u003eEngineering students working on B.Tech ECE\/EEE final-year projects or Smart India Hackathon prototypes will find the predictive maintenance use-case directly applicable. Advanced ATL Tinkering Lab participants and hobbyists preparing for VIT, IIT, or NIT tech fests can demonstrate edge AI on real industrial data. If you’ve already explored basic Arduino and want to step into TinyML, this intermediate kit bridges the gap.\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 on the box to launch the AI companion trained on this project; it can guide you step by step. If you still need a human, our WhatsApp support is included.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need any prior experience with Edge Impulse or TinyML?\u003c\/summary\u003e\u003cp\u003eNo. The AI companion walks you through creating an Edge Impulse account, connecting the ESP32-S3, collecting vibration data, and training the model. You’ll learn by doing.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I adapt this kit to monitor other types of machinery?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The same pipeline works for any rotating equipment. You can retrain the model with your own vibration data collected via the ADXL345; the AI companion shows you how.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eIs internet required after deployment?\u003c\/summary\u003e\u003cp\u003eNo, the ESP32-S3 runs the trained TensorFlow Lite model locally. Internet is only needed during the training phase in Edge Impulse; once deployed, the device operates completely offline.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eVibration data from ADXL345 trains an anomaly detection model in Edge Impulse — deployed on ESP32-S3 for real-time alerting.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/arduino-uno-r4-wifi-board-with-esp32-s3-module-ra4m1-cortex-m4\"\u003eESP32-S3 Dev Board\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/adxl345-3-axis-accelerometer-module-16g-i2cspi\"\u003eADXL345 Accelerometer\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/heltec-lora-esp32-oled-development-board-with-wifi-ble\"\u003eLED Red\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/heltec-lora-esp32-oled-development-board-with-wifi-ble\"\u003eLED Green\u003c\/a\u003e x2\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/piezoelectric-buzzer-26mm-sensor-transducer-compoden\"\u003ePiezo Buzzer\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/resistor-variety-pack-100-pcs-10-values-14w-carbon-film\"\u003e220Ω Resistors\u003c\/a\u003e x5\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/microusb-cable-1m-charging-data-cord-for-arduino-android\"\u003eMicroUSB Cable\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-M Wires x15\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 ESP32-S3 TinyML Predictive Maintenance?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The ESP32-S3 TinyML Predictive Maintenance includes all components needed: ESP32-S3 Dev Board, ADXL345 Accelerometer, LED Red, LED Green, Piezo Buzzer 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 ESP32-S3 TinyML Predictive Maintenance?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Intermediate level makers, suitable for ages 16-21. Vibration data from ADXL345 trains an anomaly detection model in Edge Impulse — deployed on ESP32-S3 for real-time alerting. Estimated build time is 5-6 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the ESP32-S3 TinyML Predictive Maintenance online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the ESP32-S3 TinyML Predictive Maintenance is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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