Technical Deep-Dive
System Architecture
Explore the technical implementation of our enterprise-grade IoT predictive maintenance platform
Repository Structure
Project Organization
predictive-care-next/
Next.js 16 Frontend (Modern UI)
Next.js 16React 19Tailwind v4Framer Motion
ml-enterprise/
Enterprise ML Backend (FastAPI + AI)
FastAPIXGBoostLightGBMCatBoostChromaDB
epics-frontend/
Original React Frontend
ReactJavaScriptCSS
epics-backend/
Original Node.js Backend
Node.jsExpressMongoDB
iot/
Arduino IoT Sensor Code
ArduinoC++DHT Sensors
ML-Model/
Original ML Notebook
JupyterPythonScikit-learn
FastAPI Backend
API Endpoints
GET
/healthHealth checkPOST
/api/predictGet failure predictionPOST
/api/predict/batchBatch predictionsPOST
/api/analyzeFull machine analysisGET
/api/recommendationsMaintenance recommendationsGET
/api/knowledge/statsKnowledge base statisticsModel Insights
Feature Importance
Understanding what drives our ensemble model's predictions
Tool Wear35%
Temperature25%
Torque22%
Rotational Speed18%
Ensemble Model
Model TypeEnsemble (Stacking)
Base ModelsXGBoost + LightGBM + CatBoost
Top FeatureTool Wear (35%)
RAG SystemChromaDB + Sentence-Transformers
Get Started
Quick Start Guide
Prerequisites
Node.js 18+Python 3.10+PostgreSQL (optional)
1. Start the ML Backend
# Navigate to ML backend
cd ml-enterprise
# Create virtual environment
python -m venv predictive_maintenance_env
.\predictive_maintenance_env\Scripts\Activate.ps1 # Windows
# Install dependencies
pip install -r requirements.txt
# Train and serve
python main.py train --model-type ensemble
python main.py serve --port 8000
2. Start the Next.js Frontend
cd predictive-care-next
npm install
npm run dev
# Visit: http://localhost:3000
Explore the Source Code
Dive into the full implementation on GitHub and see how we built this enterprise-grade IoT platform.