industrial-data

Intelligent analysis of industrial process data using neural networks, fuzzy clustering, ANFIS, and hybrid neuro-fuzzy models in MATLAB.

Dynamic system monitoring using HMM, BAFO and Monte Carlo

Data Quality Assessment for Industrial Data

Enterprise Data Warehouse for Chocolate Manufacturing using SQL Server, ETL, Data Modeling, and Industrial Data Analytics.

ASNS (AI Sensory Neural System) is an evidence-first industrial AI architecture that structures identity, time, acquisition state, lineage, and replay before data enters databases.

End-to-end industrial data pipeline built on Medallion architecture (Bronze / Silver / Gold) — SCADA ingestion, anomaly detection and Azure Blob Storage export

Process Engineering simulation tools for industrial efficiency, mass-balance calculations, and system purity optimization.

Machine learning project to predict final steel temperature in industrial production processes.

Pipeline IIoT em tempo real com MQTT, Kafka, InfluxDB e Grafana — Cenário de Mineração

Code and processed data accompanying the MSc thesis Integrating Dynamical Systems and Machine Learning for Modeling and Predicting Decay Across Wood Treatments, conducted at DTU in collaboration with the Danish Technological Institute.

A hands-on PostgreSQL dataset for learning Industrial SQL and Smart Factory analytics.

Reproducible Python workflow for simulating PLC-style machine operation data, fault events, anomaly labels, validation checks, and visual outputs.

Controle de qualidade de dados industriais com Cognite Data Fusion (Open Industrial Data / plataforma Valhall): catálogo de 363 sensores, detecção de gaps, spikes, flatline e valores fora de faixa.

Modèle de maintenance prédictive basé sur un Random Forest optimisé pour détecter les pannes industrielles (AI4I 2020)

Perfil profissional: automação industrial, manutenção e dados industriais

Reproducible Python pipeline for multi-machine manufacturing anomaly detection, automated testing and engineering reporting.

Classify SAP BOM components as plastic / polymer / rubber / non-plastic — rules + ML, with confidence, evidence, and a review queue for ambiguous or contradictory rows.

Predictive maintenance analysis of Scania vehicle components using feature engineering, PCA, and association-rule mining.

Industrial stability and risk analytics framework for solar ingot puller operations using engineered variability, control deviation (SP–PV gaps), drift features, and interpretable regression modeling.

Motor failure prediction using real industrial data (ELGi internship)