Autoclave AI Monitor: Industrial IoT with ML

An enterprise-grade AI-powered monitoring system for autoclave sterilization processes. Combines real-time sensor data acquisition, machine learning-based anomaly detection, predictive maintenance, and an interactive 3D Digital Twin visualization.

Autoclave sterilization in industrial and medical settings requires precise monitoring of pressure, temperature, and cycle parameters. Equipment failures or process anomalies can compromise sterilization quality, leading to safety risks and costly downtime. Traditional monitoring relies on manual checks and reactive maintenance.

I built an intelligent monitoring platform that shifts autoclave management from reactive to predictive:

Real-Time Sensor Integration: Live data acquisition via Modbus industrial protocol, tracking pressure, temperature, humidity, and cycle duration

ML-Powered Anomaly Detection: Isolation Forest algorithm identifies unusual patterns and potential issues before they escalate

Predictive Maintenance: Time-series forecasting models estimate equipment degradation and failure probability, enabling proactive servicing

3D Digital Twin: Interactive WebGL-powered 3D representation of the autoclave system, synchronized with live sensor data, featuring heat maps and pressure distribution visualization

Automated Alerting: Configurable notifications for anomalies and critical conditions

PDF Reporting: Automated compliance documentation and report generation for regulatory audits

REST API: Full CRUD operations with Django REST Framework, CORS support, and token authentication

Backend: Python, Django, Django REST Framework

Machine Learning: Scikit-learn, NumPy, Joblib (Isolation Forest, time-series forecasting)

Industrial Protocol: PyModbus (Modbus TCP/RTU)

Database: PostgreSQL

3D Visualization: Three.js / WebGL

Deployment: Gunicorn, WhiteNoise, Render

A production-ready industrial IoT system with 34 commits, clean modular architecture, ML pipeline for anomaly detection and predictive maintenance, and a 3D Digital Twin interface. Designed for real-world deployment in medical and industrial sterilization facilities.

GitHub: https://github.com/Davoadeivai/autoclave-ai-monitor