Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm

Autor:	Badii Gmati, Amine Ben Rhouma, Houda Meddeb, Sejir Khojet El Khil
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	open-circuit fault induction motor sigmoid function bidirectional long short-term memory deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Transportation engineering TA1001-1280
Zdroj:	World Electric Vehicle Journal, Vol 15, Iss 2, p 53 (2024)
Druh dokumentu:	article
ISSN:	15020053 2032-6653
DOI:	10.3390/wevj15020053
Popis:	Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/1795321d62fe45b2bb12851456d7b447 Zobrazit plný text záznamu View record in DOAJ