基于多传感器融合图像特征的PMSM故障诊断
Jianping Wang1, Jian Ma1, Dean Meng1
1School of Automobile, Chang'an University, Xi'an 710064, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种新的故障诊断方法,用于使用合传感器数据的永磁同步电机 (PMSM). 该技术提高了诊断的准确性和稳定性,即使在杂的工业环境中.
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:
- 永磁同步电机 (PMSM) 在工业应用中至关重要,但容易出现各种故障.
- 现有的故障诊断方法经常忽视多传感器数据融合,并与噪音数据作斗争.
- 间转短路,去磁化和轴承问题是PMSM常见的故障.
研究的目的:
- 为PMSMs提出一个强大而准确的故障诊断方法.
- 在杂的工业环境中应对多传感器信息融合的挑战.
- 通过先进的诊断来提高PMSM的可靠性和运营效率.
主要方法:
- 从PMSM上的多个位置获取振动加速信号.
- 开发一种多信号格拉米安角差距场 (MGADF) 方法,以创建合传感器图像.
- 提取和融合多纹理图像特征用于故障分类.
- 针对故障特征学习和分类的各种机器学习模型的比较.
主要成果:
- 提出的方法实现了99.54%的平均诊断准确度,低标准偏差为0.19.
- 在杂的环境中表现出卓越的性能和稳定性.
- 通过融合图像功能成功识别了PMSM故障.
结论:
- 开发的PMSM故障诊断方法提供了高精度和稳定性,特别是在具有挑战性的工业环境中.
- 使用多传感器融合的非侵入性,基于图像的方法对条件监测有效.
- 这种方法显示出在工业电机诊断中广泛应用的巨大潜力.
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