Gmati, B.; Ben Rhouma, A.; Meddeb, H.; Khojet El Khil, S. Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm. World Electr. Veh. J.2024, 15, 53.
Gmati, B.; Ben Rhouma, A.; Meddeb, H.; Khojet El Khil, S. Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm. World Electr. Veh. J. 2024, 15, 53.
Gmati, B.; Ben Rhouma, A.; Meddeb, H.; Khojet El Khil, S. Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm. World Electr. Veh. J.2024, 15, 53.
Gmati, B.; Ben Rhouma, A.; Meddeb, H.; Khojet El Khil, S. Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm. World Electr. Veh. J. 2024, 15, 53.
Abstract
Availability and continuous operation under critical conditions are very important in electric machine drive systems. However, such systems, are suffering from several type 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 diagnosis multiple open-circuit fault in three phase two level voltage source inverter for induction motor drive applications. The proposed approach uses fault diagnosis variables obtained from 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 effectiveness and robustness.
Keywords
Fault Diagnosis; Power Converters; Open-Circuit Fault; Deep Learning; AC drives
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
