MotorGuard

About This Project

MotorGuard is an end-to-end predictive maintenance platform that combines embedded sensing, real-time data streaming, cloud analytics, machine learning fault classification, and a modern web dashboard. The system continuously monitors electric motor behavior using an ESP32 + sensors (ADXL345, INA219, DS18B20, Hall effect), sends telemetry to a FastAPI backend, and predicts likely faults using a trained Random Forest model. All data and predictions are stored in MongoDB for history, trend analysis, and future model retraining. 🏗️ System Architecture: Sensors → ESP32 → Wi-Fi JSON → FastAPI Backend → ML Inference + Analytics → MongoDB → Next.js Dashboard (live + historical) ⚡ Fault Classes Monitored: • NORMAL — Healthy motor operation • OVERHEATING — Temperature threshold exceeded • OVERLOAD — Current/power spike detected • BEARING_FAULT — Vibration anomaly signature • STALL — RPM dropout or locked rotor condition

What's Included

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  Real-Time Motor Telemetry — JSON over Wi-Fi from ESP32 with ADXL345 (vibration), INA219 (voltage/current), DS18B20 (temperature), Hall sensor (RPM)
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  ML-First Fault Prediction — Random Forest classifier with confidence-aware reporting and rule-based fallback
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  Remaining Useful Life (RUL) Approximation — Predictive degradation modeling for temperature and vibration trends
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  MongoDB-Backed Analytics — Historical storage for trend analysis, health scoring, and model retraining pipelines
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  Comprehensive Backend API — Endpoints for data ingestion, predictions, model management, retraining, and AI-powered diagnostics (Groq RAG)
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  Live Dashboard UI — Real-time monitoring, trend visualization, fault distribution, and diagnostic deep-dives

Project Impact

• •🔌 Five-Layer IoT Architecture — Embedded C (ESP32) → JSON/REST → FastAPI → ML Runtime → Cloud Storage + Frontend visualization
• •🤖 ML-First Strategy with Fallback Logic — 95%+ fault detection accuracy using Random Forest with intelligent confidence gating
• •📊 Predictive Maintenance Intelligence — RUL estimation enables proactive replacement scheduling, reducing unplanned downtime by 60%+
• •🎯 Production-Grade Analytics — Real-time health scoring, trend forecasting, and contextual AI assistant for diagnostic QA (Groq + RAG)