一种基于参数优化变化模式分解的新型滚动轴承故障诊断方法,在小样本下进行特征加权重构和多目标注意力卷积神经网络的特征加权重构
Chaoqun Hu1,2, Yonghua Li1, Zhe Chen1
1College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China.
The Review of scientific instruments
|July 28, 2023
概括
这项研究引入了一种智能混合方法,用于精确的滚动轴承故障诊断,即使使用小数据集. 该方法将优化信号分解与一种新型神经网络相结合,用于更优质的故障模式和严重性识别.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 滚动轴承故障诊断对于工业机械维护至关重要.
- 准确的诊断,特别是有限的数据,仍然是一个挑战.
- 现有的方法经常在降噪和复杂的故障模式方面扎.
研究的目的:
- 提出一种智能混合方法,用于在小样本中精确地诊断滚动轴承故障.
- 改进信号处理技术,以加强故障检测.
- 开发一种强大的方法来识别故障模式和严重程度.
主要方法:
- 一个改进的变化模式分解 (VMD),由鱼优化算法优化,用于信号消噪.
- 多尺度转换 (MPE) 和最大信息系数 (MIC) 用于信号重建权重.
- 一个多目标注意力卷积神经网络 (MTACNN) 用于故障诊断.
主要成果:
- 拟议的信号处理方法显示出优越的降噪能力.
- 混合方法在故障模式识别和故障严重程度识别方面都实现了高性能.
- MTACNN有效地使用重建的信号诊断滚动轴承故障.
结论:
- 智能混合式方法提供了一个精确和有效的解决方案,用于在小样本尺寸的滚动轴承故障诊断.
- 优化的VMD,MPE-MIC权重和MTACNN的组合显著提高了诊断准确度.
- 这种方法为预测性维护和确保机械可靠性提供了有价值的工具.
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