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

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.

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

🎯 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.

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

An open-source factory intelligence platform for quality drift detection, synthetic factory simulation, governed AI workflows, and industrial operations intelligence.

Site informativo sobre a 4ª Revolução Industrial - projeto acadêmico

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.

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!

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.

End-to-end Machine Learning project simulating IoT sensor data to predict industrial equipment failures using Random Forest.

Implementation repository for a master's thesis at Technische Hochschule Ulm on Generative AI for industrial exception handling. Includes RAG and Knowledge Graph agent prototypes as part of the Skill Orchestration Agent project.

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

End-to-End Industrial AI-driven condition monitoring system for early fault detection in rotating machinery using Unsupervised LSTM Autoencoders. Detects failures 72h in advance. Early fault detection with 100% accuracy on high-frequency sensor data (NASA IMS Dataset).

Computer Vision framework for automated building pathology detection. M.Sc. Research focused on Deep Learning, Structural Health Monitoring and BIM integration.