基于MTF融合图像和NRBO-SCN方法的永久磁铁同步电机的故障诊断
Yinquan Yu1, Hongping Ge2,3, Giuseppe Carbone4
1School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China. yu_yinquan@ecjtu.edu.cn.
Scientific reports
|November 27, 2025
概括
这项研究引入了一种新的方法来诊断永久磁铁同步电机 (PMSM) 故障,使用马尔科夫过渡场 (MTF) 图像融合和牛顿-拉普森基于优化 (NRBO) 增强的随机配置网络 (SCN). 该方法准确地识别了PMSM异常度和去磁化故障.
科学领域:
- 电气工程 电气工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 常规的常磁同步电机 (PMSM) 故障诊断方法难以有效地从运行电流信号中提取关键的故障信息.
- 当前特征提取技术的局限性阻碍了对各种PMSM故障类型的准确识别.
研究的目的:
- 开发一种新的,强大的PMSM故障诊断方法,克服现有的特征提取技术的局限性.
- 提高PMSM中故障识别的准确性和一般化能力.
主要方法:
- 开发了一个高保真性PMSM模拟模型,提取三相电流信号作为故障指示器.
- 三相电流信号被转化为二维马尔科夫过渡场 (MTF) 图像,随后融为RGB图像.
- 从MTF融合图像中提取了颜色,形状和纹理特征,以构建故障信息特征向量.
- 一个随机配置网络 (SCN) 模型使用牛顿-拉普森基于优化 (NRBO) 算法进行了优化,用于增强分类.
- 提取的故障特征向量被输入到NRBO-SCN模型中用于故障识别.
主要成果:
- 与其他现有方法相比,拟议的方法显示出优越的故障分类性能.
- 实现了PMSM故障的准确识别,特别是离心率和去磁化.
- NRBO优化显著改善了SCN模型的概括和故障识别能力.
结论:
- 集成的MTF图像融合和NRBO-SCN方法为PMSM提供了高度有效和准确的故障诊断解决方案.
- 这种新的方法在确保PMSM的可靠性和运行完整性方面取得了重大进展.
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