使用维格纳-维尔分布进行故障诊断的电机振动信号分类
Jian-Da Wu1, Wen-Jun Luo2, Kai-Chao Yao2
1Graduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua 50007, Taiwan.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究引入了一种新的方法来诊断无刷电机故障,使用Wigner-Ville分布 (WVD) 和YOLO深度学习进行振动信号分类. 这种方法提高了准确性,而不需要拆卸电机.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 传统的故障诊断依赖于时间和频率域特征,这些特征可能是有限的.
- 噪音和振动信号的分类对于机械和电子系统,包括电动汽车至关重要.
- 无刷电机的准确故障诊断对于可靠性和稳定性至关重要.
研究的目的:
- 提出和验证一种用于可视化和分类无刷电机振动信号的新技术.
- 使用维格纳-维尔分布 (WVD) 方法提取振动信号特征.
- 利用人工神经网络,特别是YOLO (你只看一次) 深度学习模型,用于信号分类.
主要方法:
- 来自在六个不同的革命状态下运行的无刷电机的振动信号被测量.
- 使用维格纳-维尔分布 (WVD) 方法将振动信号转换成图像并可视化.
- 用YOLO (你只看一次) 深度学习模型来识别和分类这些WVD图像用于故障诊断.
主要成果:
- 该WVD方法有效地从无刷电机中提取出明显的振动信号特征.
- YOLO深度学习模型成功识别和分类了WVD图像,证明了该方法的可行性.
- 对瓦格纳方法参数和识别率的分析表明,改善了电机故障诊断能力.
结论:
- 拟议的方法使无刷电机的准确故障诊断能够在不需要拆卸的情况下实现.
- 这种技术通过提高诊断准确度来提高无刷电机应用的可靠性和稳定性.
- 集成WVD可视化和YOLO深度学习为状态监测和故障诊断提供了有前途的进步.
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