Model-Agnostic Meta-Learning for Fault Diagnosis of Induction Motors in Data-Scarce Environments with Varying Operating Conditions and Electric Drive Noise

IF 5 2区工程技术 Q2 ENERGY & FUELS

IEEE Transactions on Energy Conversion Pub Date : 2025-03-29 DOI:10.1109/tec.2025.3556100

Ali Pourghoraba, MohammadSadegh KhajueeZadeh, Ali Amini, Abolfazl Vahedi, Gholam Reza Agah, Akbar Rahideh

引用次数: 0

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基于模型不可知元学习的异步电动机故障诊断

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Energy Conversion 工程技术-工程：电子与电气

CiteScore

11.10

自引率

10.20%

发文量

230

审稿时长

4.2 months

期刊介绍： The IEEE Transactions on Energy Conversion includes in its venue the research, development, design, application, construction, installation, operation, analysis and control of electric power generating and energy storage equipment (along with conventional, cogeneration, nuclear, distributed or renewable sources, central station and grid connection). The scope also includes electromechanical energy conversion, electric machinery, devices, systems and facilities for the safe, reliable, and economic generation and utilization of electrical energy for general industrial, commercial, public, and domestic consumption of electrical energy.