grupo de supervisión y diagnóstico de procesos industriales
GSDPI

The goal of our research is the development and application of visual analytics techniques focused to process analysis and monitoring, to allow the user: 1) discover the factors that affect efficiency and 2) assess the process working condition, by means of an efficient combination of intelligent data analysis algorithms, data visualization and interaction mechanisms

In essence, this approach harness the powerful abilities of the human visual system to detect patterns and its quality as an optimal vehicle for knowledge acquisition, turning large volumes of data and information into interactive visualizations that allow detection of patterns, data understanding and discovering useful knowledge by means of an exploratory process where the user is actively involved.

• Visual Analytics
• Data visualization
• Interactive visual displays
• Interactive machine learning (iML)
• Neural Networks & Machine Learning
• Deep CNN's
• Deep autoencoders (AE, VAE, denoising AE)
• Generative models (GAN)
• Echo state networks (ESN)
• Self-organizing maps (SOM)
• Dimensionality reduction
• Clustering and classification
• Process Data Analytics
• Process monitoring and control
• Fault Detection and Identification (FDI)
• Process modeling and System Identification
• Virtual sensors, estimation and forecasting
• Energy disaggregation