基于深度学习方法的三相永磁同步电机 (PMSM) 中的离心率故障诊断系统
Kenny Sau Kang Chu1, Kuew Wai Chew1, Yoong Choon Chang1
1Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43200, Malaysia.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了特异性故障诊断网络 (E-FDNet),用于早期检测运动特异性故障. 这种新的系统使用混合CNN-LSTM神经网络实现了高精度,提高了电机的可靠性.
科学领域:
- 电气工程 电气工程
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 引擎偏心故障是由旋转器错位引起的,会引起振动和噪音,降低引擎的可靠性.
- 早期发现和纠正这些故障对于保持运营效率至关重要.
研究的目的:
- 提出一个新的异心差故障诊断网络 (E-FDNet) 进行高效的电机异心差故障检测.
- 为了实际应用,将E-FDNet集成到发动机偏心故障诊断系统 (MEFDS) 中.
主要方法:
- 开发了一种混合卷积神经网络-长期短期记忆 (CNN-LSTM) 架构,用于故障检测.
- 引入了稳定状态特征规范化 (SSCN) 来提高特征的一致性.
- 使用一个集成的物理-有限元法 (FEM) 实验管道进行验证.
主要成果:
- 对于静态,动态和混合离心率断层,E-FDNet显示了稳定的过渡预测.
- 获得了大约98.86%的准确性和F1分数,超过了现有的方法.
- 该系统采用非侵入性,仅电流传感设计,方便部署.
结论:
- 拟议的E-FDNet由CNN-LSTM网络提供动力,为电机离心率故障诊断提供高度准确和可靠的解决方案.
- 非侵入性,基于电流的方法使该系统适合于现实世界的应用,改善运动诊断.
相关概念视频
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