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AI-Powered Predictive Maintenance & Fault Diagnosis through Model Context Protocol. An open-source framework for integrating Large Language Models with predictive maintenance and fault diagnosis workflows.
An advanced Industrial IoT (IIoT) simulator for Smart Factory 4.0 environments using Python, MQTT, and Docker. Emulates configurable production lines with realistic sensor data (vibration, temperature, quality) and predictive alerts.
A PHP library that talks OPC UA binary protocol over TCP. It handles the full stack — transport, encoding, secure channels, sessions, crypto — so you can connect to any OPC UA server straight from PHP, without shelling out to C/C++ libraries.
Features a logic-gate to pause updates and notify stakeholders if maintenance records are incomplete.Simultaneously, machine learning models are used in retool to provide early warnings and suggestions.
An automated industrial quality control pipeline. Integrates YOLOv8 computer vision with Siemens PLCs via Snap7, utilizing encoder-synchronized FIFO shift registers for high-precision fabric defect marking.
Empowering OPC UA as the Model Context Protocol (MCP) for industrial intelligence. It enables AI Agents to seamlessly discover, connect, and intelligently control automation hardware.
Computer Vision framework for automated building pathology detection. M.Sc. Research focused on Deep Learning, Structural Health Monitoring and BIM integration.
Predictive Fault Detection System for Three-Phase Induction Motors using Thermography and CNNs with Grad-CAM Interpretability | Bachelor's Thesis | UFSC 2025
A Real-Time Manufacturing Intelligence Dashboard integrating a Decision Support System (DSS) and Expert System for predictive maintenance, energy optimization, and smart factory diagnostics. Built with React, Tailwind CSS, and Hybrid Intelligence logic.