• English
    • español
  • español 
    • English
    • español
  • Login
Ver ítem 
  •   DSpace Principal
  • 2.- Investigación
  • Artículos
  • Ver ítem
  •   DSpace Principal
  • 2.- Investigación
  • Artículos
  • Ver ítem
JavaScript is disabled for your browser. Some features of this site may not work without it.

Anomaly detection method based on the deep knowledge behind behavior patterns in industrial components. Application to a hydropower plant

Thumbnail
Ver/
IIT-20-185A.pdf (5.738Mb)
Fecha
2021-02-01
Autor
Calvo Báscones, Pablo
Sanz Bobi, Miguel Ángel
Welte, Thomas Michael
Estado
info:eu-repo/semantics/publishedVersion
Metadatos
Mostrar el registro completo del ítem
Mostrar METS del ítem
Ver registro en CKH

Refworks Export

Resumen
 
 
This paper describes a new methodology that aims to cover a gap detected in the area of detection of anomalies and diagnosis of industrial component behaviors: there is a need of robust procedures compatible with dynamic behaviors and degradations that evolve over time. The method proposed is based on the creation of behavior patterns of industrial components using well-known unsupervised machine learning algorithms such as K-means and Self-Organizing maps (SOMs) as a starting point. An algorithm based on local Probability Density Distributions (PDD) of the clusters obtained is used to enhance the characterization of patterns. The joint use of these algorithms facilitates a new way to detect anomalies and the surveillance of their progress. The paper includes an example of an application of the method proposed for monitoring the bearing temperature of a turbine in a hydropower plant showing how this method can be applied in behavior and maintenance assessment applications. The results obtained prove the advantages and possibilities that the proposed methodology has on real world applications.
 
URI
https:doi.org10.1016j.compind.2020.103376
Anomaly detection method based on the deep knowledge behind behavior patterns in industrial components. Application to a hydropower plant
Tipo de Actividad
Artículos en revistas
ISSN
0166-3615
Materias/ categorías / ODS
Instituto de Investigación Tecnológica (IIT)
Palabras Clave

Anomaly detection; pattern discovery; normal behavior characterization; maintenance assessmen; self-organizing maps; k-means; probability density functions; hydropower plant
Colecciones
  • Artículos

Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias
 

 

Búsqueda semántica (CKH Explorer)


Listar

Todo DSpaceComunidades & ColeccionesPor fecha de publicaciónAutoresTítulosMateriasPor DirectorPor tipoEsta colecciónPor fecha de publicaciónAutoresTítulosMateriasPor DirectorPor tipo

Mi cuenta

AccederRegistro

Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias