{"product_id":"kit-pi-5-edge-ai-anomaly-detection","title":"Pi 5 Edge AI Anomaly Detection Kit with Raspberry Pi 5 + LED","description":"\u003ch1\u003eBuild a Real-Time Edge AI Anomaly Detection System with Raspberry Pi 5\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 6-8 hours\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-21 years\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Edge AI, Machine Learning\u003c\/span\u003e\n\u003c\/div\u003e\n \n\u003cp\u003eYou’re about to build a real-time machine monitoring system that catches faults before they cause failures. The Raspberry Pi 5 reads vibration data from the ADXL345 accelerometer, runs an Edge Impulse anomaly model, and triggers an alert when something goes wrong — all at the edge, no cloud required. It’s the exact kind of predictive maintenance prototype a Smart India Hackathon team or final-year B.Tech student needs to stand out.\u003c\/p\u003e\n \n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA self-contained predictive maintenance node that bolts onto industrial equipment like motors, pumps, or conveyor belts. It logs vibration patterns to MicroSD with timestamps (RTC), displays status on the OLED, and sounds a buzzer when vibration deviates from learned norms. The system learns what “normal” looks like from your own machine, so it adapts to your specific setup — not a generic threshold.\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 sensor data from the ADXL345\u003c\/li\u003e\n  \u003cli\u003eInterface the ADXL345 accelerometer over I2C on Raspberry Pi 5 GPIO\u003c\/li\u003e\n  \u003cli\u003eDeploy a TensorFlow Lite model on Raspberry Pi 5 for real-time inference at the edge\u003c\/li\u003e\n  \u003cli\u003eImplement timestamped data logging with DS3231 RTC and visual feedback on a 0.96-inch OLED\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\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eADXL345 Accel\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eDS3231 RTC\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e0.96in OLED\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eActive Buzzer\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e4.7kΩ Resistors\u003c\/td\u003e\n\u003ctd\u003ex5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e100nF Ceramic Capacitors\u003c\/td\u003e\n\u003ctd\u003ex5\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003e40-pin GPIO Header\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eM-F Wires\u003c\/td\u003e\n\u003ctd\u003ex20\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eMicroSD Card 32GB\u003c\/td\u003e\n\u003ctd\u003ex1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003ex1\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 pursuing B.Tech in ECE\/EEE who need an advanced project for their final semester, participants in Smart India Hackathon building industrial IoT solutions, and ATL tinkering labs mentoring older students. If you’re comfortable with Python and the Linux terminal, this kit gives you a real edge-AI experience that goes far beyond a blinking LED.\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\u003eOpen the QR code on the kit box to start a chat with the AI companion that knows every wire and line of code for this project, or message our Bengaluru team on WhatsApp for direct help.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior experience with machine learning?\u003c\/summary\u003e\u003cp\u003eNot necessarily. The AI companion walks you through collecting vibration data, uploading it to Edge Impulse, and training the anomaly model. Some familiarity with Python and Raspberry Pi is helpful, but you’ll learn the ML workflow as you build.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I adapt the anomaly model for different sensors or machines?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The architecture is modular — once you’ve built the ADXL345 pipeline, you can swap in other I2C sensors like the MPU6050. The companion will guide you on relabelling data and retraining for new patterns.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat if a component arrives faulty?\u003c\/summary\u003e\u003cp\u003eWe test every kit before shipping, but if a manufacturing defect slips through, we’ll replace the part at no cost within 7 days of delivery. Just message us on WhatsApp or email — our support is in Bengaluru, not an overseas bot.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003ePi 5 runs Edge Impulse anomaly model on ADXL345 vibration data from GPIO. Flags machine faults in real time.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/adxl345-3-axis-accelerometer-module-16g-i2cspi\"\u003eADXL345 Accel\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/ds3231-real-time-clock-module-i2c-rtc-with-battery-backup\"\u003eDS3231 RTC\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\/active-buzzer-dc-3-5v-85db-sounder-module-for-arduino-raspberry-pi\"\u003eActive Buzzer\u003c\/a\u003e\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\/capacitor-variety-pack-6-values-100nf-to-470uf-30-pieces\"\u003e100nF Caps\u003c\/a\u003e x5\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-400-gpio-header-adapter-40-pin-breakout\"\u003e40-pin GPIO Header\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003eM-F Wires x20\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/microsd-card-reader-spi-module-for-arduino\"\u003eMicroSD Card 32GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\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 Pi 5 Edge AI Anomaly Detection Kit with Raspberry Pi 5 + LED?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Edge AI Anomaly Detection Kit with Raspberry Pi 5 + LED includes all components needed: Raspberry Pi 5 4GB, ADXL345 Accel, DS3231 RTC, 0.96in OLED, Active 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 Pi 5 Edge AI Anomaly Detection Kit with Raspberry Pi 5 + LED?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-21. Pi 5 runs Edge Impulse anomaly model on ADXL345 vibration data from GPIO. Flags machine faults in real time. Estimated build time is 6-8 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Edge AI Anomaly Detection Kit with Raspberry Pi 5 + LED online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Edge AI Anomaly Detection Kit with Raspberry Pi 5 + LED is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. 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