grupo de supervisión y diagnóstico de procesos industriales

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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.

These techniques can be used to improve efficiency of processes, by means of an exploratory analysis of the relationships between the factors -sensor data, process parameters- and performance -quality indices, efficiency-, as well as for fault detection and monitoring of efficiency.

visual analytics, data visualization, process modeling, predictive analysis, dimensionality reduction, energy efficiency

Research topics:
  • Visual Analytics
  • Process Data Analytics
  • Condition Monitoring
  • Dimensionality Reduction
  • Fault Detection and Identification
  • Neural Networks
  • Self Organizing Maps (SOM)
  • Radial Basis Functions (RBF)
  • Kernel Regression
  • Digital Signal Processing (DSP)
  • Data and information visualization for process analysis
  • Interactive data visualization interfaces
  • Intelligent data analysis in industrial processes
  • Process monitoring and control; energy efficiency improvement
  • Fault detection and identification (FDI)
  • Virtual sensors, estimation and forecasting