报告详情
Research on demagnetization fault diagnosis method of permanent magnet wind turbine based on ResNet-50 network transfer learning model
编号：92
访问权限：仅限参会人
更新：2024-10-23 10:33:51
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口头报告
报告开始：2024年11月02日 09:10（Asia/Shanghai）
报告时间：20min
所在会场：[P4] Parallel Session 4 [P4-2] Parallel Session 4（November 2 AM）
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摘要
Abstract—Residual networks (ResNet) in convolutional neural networks (CNN) were used for demagnetization fault image recognition and classification. Initially, we collected a dataset of demagnetization fault characteristics in permanent magnet wind generators. Using this dataset, a ResNet50 fault diagnosis model was developed for a 25kW wind turbine. This model was then transferred to a 2MW permanent magnet wind generator using deep transfer learning. Compared to models without transfer learning, this approach significantly improved fault diagnosis accuracy and efficiency.
关键词
Permanent Magnet Wind Turbines,Fault Diagnosis,Transfer Learning,Convolutional Neural Networks Introduction
报告人
DuSiyu
Student Xi'an Jiaotong University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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