A Comparison of Model-Based and Machine Learning Techniques for Fault Diagnosis

Dash, Balyogi Mohan; Bouamama, Belkacem Ould; Boukerdja, Mahdi; Pekpe, Komi Midzodzi

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

DOI :

10.1109/MEPCON55441.2022.10021712

Title :

A Comparison of Model-Based and Machine Learning Techniques for Fault Diagnosis

Author(s) :

Dash, Balyogi Mohan [Auteur]
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]

Conference title :

2022 23rd International Middle East Power Systems Conference (MEPCON)

City :

Cairo

Country :

Égypte

Start date of the conference :

2022-12-13

Publisher :

IEEE

English keyword(s) :

Hybrid fault diagnosis, bond graph, machine learning, storage device

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

In recent years, there has been a lot of interest in Fault Detection and Isolation (FDI) for systems. Model-based methods and Machine Learning (ML)-based approaches have been extensively developed to detect and identify ...
Show more >In recent years, there has been a lot of interest in Fault Detection and Isolation (FDI) for systems. Model-based methods and Machine Learning (ML)-based approaches have been extensively developed to detect and identify specific faults by taking into consideration, respectively, the mathematical description of the monitored process and the statistical model constructed from historical data. Recently, studies have been conducted to combine both approaches to improve FDI performance. This study provides a side-by-side comparison of both approaches on the same system, which will aid in determining the best way to combine both approaches to create a hybrid FDI. First, the current state of the art in model-based, ML-based, and hybrid FDI is reviewed. Second, the detailed experimental setup and principles of both FDI approaches are discussed. The FDI of an actual Storage Device (SD) utilized in a green hydrogen production platform is then performed using both methodologies. Finally, it is stated that while both approaches have advantages and disadvantages, they can be combined to complement each other and improve the FDI performance.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL

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