Efficient residuals pre-processing for diagnosing multi-class faults in a doubly fed induction generator, under missing data scenarios

RAZAVI FAR, ROOZBEH;ZIO, ENRICO;
2014-01-01

2014
Fault diagnosis; Latent residuals; New class faults; NIPALS; Wind turbine; Wold cross-validation; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; Engineering (all)
File in questo prodotto:
File Dimensione Formato  
Efficient residuals pre-processing for diagnosing multi-class faults_11311-968315_Zio.pdf

accesso aperto

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 5.27 MB
Formato Adobe PDF
5.27 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/968315
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 30
  • ???jsp.display-item.citation.isi??? 26
social impact