基于模型的转移学习方法用于检测PMSM中的互转短路故障
Mingsheng Wang1, Qiang Song1, Wuxuan Lai1
1National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology (BIT), Beijing 100081, China.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
在永磁同步电机 (PMSM) 中早期检测间转短路 (ITSC) 故障至关重要. 本研究介绍了一种基于模型的新型转移学习方法,用于准确的ITSC故障检测,其性能优于现有的方法.
科学领域:
- 电气工程 电气工程
- 机器学习 机器学习
- 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断 错误诊断
背景情况:
- 常磁同步电机 (PMSM) 中的间转短路 (ITSC) 故障存在重大风险,可能导致灾难性故障.
- 早期和准确地检测ITSC故障对于确保PMSM的可靠性和安全性至关重要.
研究的目的:
- 开发和验证基于模型的转移学习方法,用于早期检测PMSM中的ITSC故障.
- 为故障检测模型预培训提出一个强大的深度学习架构.
主要方法:
- 一个贝叶斯优化的剩余扩展卷积神经网络 (CNN) 已被开发用于预训练,利用扩展卷积用于扩展受体场和剩余架构以减轻退化.
- 使用贝叶斯优化解决了超参数调整问题.
- 实施了转移学习框架,以使预训练模型适应ITSC故障检测的新目标域数据集.
主要成果:
- 实验验证证明了拟议的基于模型的转移学习方法对ITSC故障检测的有效性.
- 与其他七种现有的故障检测技术相比,提出的方法显示出更高的性能.
- 剩余扩张CNN和转移学习的结合证明在PMSM错误诊断方面具有优势.
结论:
- 开发的基于模型的转移学习方法为可靠和早期检测PMSM中的ITSC故障提供了有希望的解决方案.
- 贝叶斯优化的剩余扩展CNN为故障检测模型提供了坚实的基础.
- 这种方法提高了PMSM在各种应用中的安全性和运行完整性.
相关概念视频
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