Memorias de investigación
Ponencias en congresos:
Self-learning of fault diagnosis identification
Año:2011
Áreas de investigación
  • Tecnología química,
  • Automática
Datos
Descripción
A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.
Internacional
Si
Nombre congreso
21st European Symposium on Computer Aided Process Engineering (ESCAPE 21)
Tipo de participación
960
Lugar del congreso
Chalkidiki (Grecia)
Revisores
Si
ISBN o ISSN
978-0-444-53895-6
DOI
Fecha inicio congreso
29/05/2011
Fecha fin congreso
01/06/2011
Desde la página
885
Hasta la página
889
Título de las actas
Computer Aided Chemical Engineering, Vol. 29 (21st European Symposium on Computer Aided Process Engineering)
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Laboratorio de Sistemas Autónomos
  • Departamento: Automática, Ingeniería Electrónica e Informática Industrial
  • Departamento: Ingeniería Química Industrial y del Medio Ambiente