Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Robotics and Control Systems

Dynamic Assessment and Control of a Dual Star Induction Machine State Dedicated to an Electric Vehicle Under Short-Circuit Defect Benbouya, Basma; Cheghib, Hocine; Behim, Meriem; Mahmoud, Mohamed Metwally; Elnaggar, Mohamed F.; Ibrahim, Nagwa F.; Anwer, Noha
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1557

Abstract

The widespread use of electric vehicles (EVs) in several industries gives rise to many significant safety and reliability-related issues. Thus, there is a need for methods for identifying flaws in EV components. In this paper, a state assessment of a dual star induction machine (DSIM) under short-circuit faults is investigated. The DSIM is selected due to its widespread use in high-power applications and its numerous advantages over other conventional machine types. Our focus is particularly on its application in the automotive industry, where its dual stator windings ensure reliable and robust parallel operation, thereby enhancing its robustness and efficiency. To improve this technology and ensure its proper functioning following potential failures and during maintenance, appropriate diagnostic and monitoring methods are essential. Our methodology combines two techniques: the current space vector (CSV), utilized to prevent information loss, and the wavelet packet decomposition energy, calculated from the resulting CSV signals. This approach enables the detection of various stator short-circuit faults, presenting different severities and occurring at different locations. The outcomes of this study, which were verified through the use of a Simulink model of a DSIM devoted to an EV, showcase the efficacy of the suggested approach. Furthermore, this work underscores the significance of this approach in maintaining the performance and reliability of DSIM, particularly in demanding environments such as the automotive industry.
Utilizing Short-Time Fourier Transform for the Diagnosis of Rotor Bar Faults in Induction Motors Under Direct Torque Control Bousseksou, Radouane; Bessous, Noureddine; Elzein, I. M.; Mahmoud, Mohamed Metwally; Ma'arif, Alfian; Touti, Ezzeddine; Al-Quraan, Ayman; Anwer, Noha
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1886

Abstract

Industrial applications rely heavily on induction motors (IMs). Even though any IM problem can seriously impair operation, rotor bar failures (RBFs) are among the toughest to identify because of their detection challenges. RBFs in IMs can significantly impact performance, leading to reduced efficiency, increased vibrations, and potential IM failure. This research provides a thorough analysis of diagnosing these issues by detecting RBFs and evaluating their severity using three sophisticated signal processing techniques (Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT)). The three techniques (FFT, DWT, and STFT) are used in this work to assess the stator currents. An accurate mathematical model of the IM under RBFs serves as the basis for the simulation. The robustness of Direct Torque Control (DTC) is assessed by examining the IM's behavior in both normal and malfunctioning situations. Although the results show that DTC successfully preserves motor stability even when there are flaws, the current analysis offers some significant variation. The findings show that when it comes to identifying RBFs in IMs and determining their severity, the STFT performs better than FFT and DWT. The suggested method maintains low estimation errors and strong performance under various operating situations while providing high failure detection accuracy and the ability to discriminate between RBFs.