Partager

Document IIF

Méthode de diagnostic semi-supervisé des défaillances cachées des compresseurs frigorifiques basée sur un modèle d'autoencodeur de transformateur convolutionnel.

Semi-supervised diagnosis method of refrigeration compressor hidden defect based on convolutional transformer autoencoder model.

Auteurs : LI K., JIN H., XU Y., GU J., HUANG Y., SHI L., YAO Q., SHEN X.

Type d'article : Article de la RIF

Résumé

Improving product quality and service life by diagnosing defects in compressors through vibration signal analysis of the compressor's shell is challenging due to strong noise interference and limited labeled samples. The paper proposed a semi-supervised diagnosis method based on the convolution Transformer autoencoder (CTAE) for diagnosing hidden defects with small samples and strong noise. Firstly, an optimized variational modal decomposition is utilized to decouple and reduce noise from the raw vibrations, and the decomposition results are concatenated with the vibration signals as input to the model; Secondly, the CTAE is employed to learn the feature distribution of unlabeled samples and to extract and fuse local and global features from the input data; Finally, a labeled samples are used to fine-tune the model and to fuse features from multi-sensor information. The results of using a compressor dataset for validation show that the proposed method has high diagnosis accuracy and robustness with limited labeled samples and different signal-to-noise ratios.

Documents disponibles

Format PDF

Pages : 47-57

Disponible

  • Prix public

    20 €

  • Prix membre*

    Gratuit

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Semi-supervised diagnosis method of refrigeration compressor hidden defect based on convolutional transformer autoencoder model.
  • Identifiant de la fiche : 30032112
  • Langues : Anglais
  • Sujet : Technologie
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 158
  • Date d'édition : 02/2024
  • DOI : http://dx.doi.org/10.1016/j.ijrefrig.2023.10.021

Liens


Voir d'autres articles du même numéro (36)
Voir la source

Indexation