Titre :

Bond Graph-CNN based hybrid fault diagnosis with minimum labeled data

Auteur(s) :

Dash, Balyogi Mohan [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Bouamama, Belkacem Ould [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Boukerdja, Mahdi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Pekpe, Komi Midzodzi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la revue :

Engineering Applications of Artificial Intelligence

Pagination :

107734

Éditeur :

Elsevier

Date de publication :

2023-12

ISSN :

0952-1976

Mot(s)-clé(s) en anglais :

Convolutional Neural Network
Hybrid fault diagnosis
Bond graph
Multiple simultaneous faults
Structural analysis

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Automatique / Robotique
Informatique [cs]

Résumé en anglais : [en]

Fault Isolation is a critical step in any fault diagnosis method, and the difficulty increases with the complexity of the system. When there are not enough sensors in the system, the traditional model-based methods have ...
Lire la suite >Fault Isolation is a critical step in any fault diagnosis method, and the difficulty increases with the complexity of the system. When there are not enough sensors in the system, the traditional model-based methods have trouble isolating the fault. The current data-driven FDI techniques generally emphasize accuracy and rarely draw attention to the lack of readily accessible labeled data in the industry. This study aims to develop a hybrid fault diagnosis method by combining the well-established graphical technique of Bond-Graph (BG) with the powerful pattern recognition ability of Convolutional Neural Network (CNN) to improve the overall fault isolation performance. A new formalism named BG-CNN method is proposed, which can utilize the residuals generated from the BG model in a CNN for improved fault isolation with a minimal number of labeled data. The single incipient faults as well as multiple simultaneous faults can be isolated by this method. The BG-CNN method demonstrates a high level of performance for the FDI of a Direct Current (DC)-motor with a relatively small number of labeled samples. In comparison, the traditional CNN method using raw sensor data requires a significantly larger number of labeled samples to achieve a similar level of performance.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL