微电机的故障识别方法使用优化的基于CNN的JMD-GRM方法
Yufang Bai1, Zhengyang Gu1, Junsong Yu2
1College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China.
Micromachines
|January 28, 2026
概括
这项研究引入了一种新的方法来诊断微电机故障,使用Jump加AM-FM模式分解 (JMD) 和优化卷积神经网络 (OCNN). 该方法在识别微型电机缺陷方面达到99%以上的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 微型电机在工业应用中至关重要,需要强大的故障诊断以确保运行安全.
- 微电机故障信号的弱信号强度和模两可的特征带来了重大的诊断挑战.
研究的目的:
- 为微型电机开发一种新且有效的故障诊断方法.
- 解决现有方法在处理弱和模两可的故障信号方面的局限性.
主要方法:
- 利用跳转加AM-FM模式分解 (JMD) 将信号分解为AM-FM和跳转组件,提取和集成故障特征.
- 采用全球关系矩阵 (GRM) 来将1D信号转换为2D图像,以增强特征表示.
- 应用了一个优化的卷积神经网络 (OCNN) 与一个AdamW优化器,以实现准确的故障分类和减少过度拟合.
主要成果:
- 提出的方法在多种故障类型中实现了99.0476%的平均诊断准确率.
- 与其他四种故障诊断方法相比,表现出优越的性能.
- 成功地抑制了 modal aliasing 和增强了故障特征表示.
结论:
- 开发的故障诊断方法为制造业中微型电机质量检查提供了可靠的解决方案.
- 结合JMD,GRM和OCNN,为分析复杂的故障信号提供了一种强大的方法.
- 这种技术显著提高了微电机故障诊断的准确性和效率.
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