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.

MCP_HACK//26 — Industrial Zero-Trust AI: T-IA Connect + AgentGateway for Siemens TIA Portal

OPC UA industrial library for executing commands, reads and writes on OPC UA servers

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.

A streaming Digital Twin of a steel hot rolling mill demonstrating Online Machine Learning (OML) with Apache Kafka, Apache Flink and MOA to handle real-time concept drift.

🎯 AI-powered CNC production monitoring: Automatically tracks machining cycles, optimizes efficiency, reduces costs 15-25%. Real-time analytics, predictive maintenance alerts, seamless MES integration. Industrial-grade security, 99.8% uptime. ROI in 6-12 months. Ready for Industry 4.0.

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.

AI control fabric for physical systems. Visual pipeline orchestration from LLM reasoning to real hardware — PLCs, ESP32, Pico, Arduino, Raspberry Pi — runs fully local.

Real-time CNC machine failure detection using ML on AWS SageMaker | F1 ≥ 0.85 | P95 Latency ≤ 50ms | Live Streamlit Dashboard

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.

Site informativo sobre a 4ª Revolução Industrial - projeto acadêmico

Mantenimiento Predictivo Industrial que integra Ing. Mecánica y ML. Modelo XGBoost optimizado para reducir costes por rotura (€7.8k ahorro). Incluye Feature Engineering con base física, diagnóstico multiclase de fallos (HDF, OSF, PWF, TWF) y despliegue interactivo en Streamlit. Enfoque 100% orientado a negocio y fiabilidad operativa.

Industrial IoT Educational Bridge - ESP32 to React Dashboard via MQTT. Learn Industry 4.0 concepts: Edge Computing, MQTT, InfluxDB, Real-time Visualization. Perfect for technical students!

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.

Empirical performance comparison of Nanos unikernels vs. Docker containers for resource-constrained edge workloads using Go and Node.js

PLC-based real-time bearing fault detection using vibration analysis, FFT, Envelope Spectrum, and Spectral Kurtosis (TwinCAT + MATLAB)

Lightweight MQTT 3.1.1 broker built from scratch on raw TCP in Go for edge computing in Industry 4.0. Receives data from up to 5 clients, persists to SQLite via FIFO queues, and dispatches to pluggable workers. Zero external runtime dependencies.

Predictive Fault Detection System for Three-Phase Induction Motors using Thermography and CNNs with Grad-CAM Interpretability | Bachelor's Thesis | UFSC 2025

Industrial IoT monitoring system for real-time sensor data collection, visualization and predictive maintenance.