Proposition et étude expérimentale d'une méthode de diagnostic d'un compresseur frigorifique hermétique en utilisant la fusion d'images double temps-fréquence.
Proposal and Experimental Study on a Diagnosis Method for Hermetic Refrigeration Compressor Using Dual Time-Frequency Image Fusion.
Résumé
The hermetic refrigeration compressor is the core component of the refrigeration system, failure of which will cause energy waste and reduce service life. Fault diagnosis based on vibration signal is a research hotspot. However, it is challenging to extract features of nonlinear and nonstationary vibration signals, which severely restricts the development of this method. This paper proposes a dual time-frequency images fusion method to obtain more effective features for diagnosing compressor manufacturing defects. Firstly, two time-frequency images are obtained by implementing continuous wavelet transform and Hilbert-Huang transform of the same vibration signal sample. Then, a convolutional neural network is used for image feature extraction and fusion, where the features extracted from two time-frequency images have complementarity. A data set containing six categories of typical manufacturing defects is used to verify the proposed method. The results show that the average diagnostic accuracy of the proposed method reaches 95.9%, and the proposed method has a better performance than other methods.
Documents disponibles
Format PDF
Disponible
Gratuit
Détails
- Titre original : Proposal and Experimental Study on a Diagnosis Method for Hermetic Refrigeration Compressor Using Dual Time-Frequency Image Fusion.
- Identifiant de la fiche : 30029585
- Langues : Anglais
- Sujet : Technologie
- Source : Applied Sciences - vol. 12 - n. 6
- Éditeurs : MDPI
- Date d'édition : 03/2022
- DOI : http://dx.doi.org/10.3390/app12063033
Liens
Voir d'autres articles du même numéro (4)
Voir la source
Indexation
- Thèmes : Compresseurs
- Mots-clés : Détection; Panne; Compresseur hermétique; Experimentation; Apprentissage automatique; Réseau neuronal artificiel
-
Parallel deep neural network for scalable coupl...
- Auteurs : CHEN S., LIU Z., CHEN K., ZHU X., JIN X., DU Z.
- Date : 21/08/2023
- Langues : Anglais
- Source : Proceedings of the 26th IIR International Congress of Refrigeration: Paris , France, August 21-25, 2023.
- Formats : PDF
Voir la fiche
-
Deep learning-based refrigerant charge fault de...
- Auteurs : EOM Y. H., HONG S. B., YOO J. W., KIM M. S.
- Date : 31/08/2021
- Langues : Anglais
- Source : 13th IEA Heat Pump Conference 2021: Heat Pumps – Mission for the Green World. Conference proceedings [full papers]
- Formats : PDF
Voir la fiche
-
Fault detection for vaccine refrigeration via c...
- Auteurs : ABHIRAMAN B., FOTIS R., ESKIN L., RUBIN H.
- Date : 05/2023
- Langues : Anglais
- Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 149
- Formats : PDF
Voir la fiche
-
Prediction of Date Fruit Quality Attributes dur...
- Auteurs : MOHAMMED M., MUNIR M., ALJABR A.
- Date : 06/2022
- Langues : Anglais
- Source : Foods - vol. 11 - n. 11
- Formats : PDF
Voir la fiche
-
Semi-supervised diagnosis method of refrigerati...
- Auteurs : LI K., JIN H., XU Y., GU J., HUANG Y., SHI L., YAO Q., SHEN X.
- Date : 02/2024
- Langues : Anglais
- Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 158
- Formats : PDF
Voir la fiche