在小样本下基于多传感器双层信息融合的滚动轴承故障诊断的研究
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
|November 8, 2023
概括
这项研究引入了一种新的多传感器融合方法,用于在有限的数据下准确地诊断滚动轴承故障. 该方法提高了诊断的准确性和稳定性,优于现有的方法.
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:
- 由于数据不足,单传感器轴承故障诊断的准确性较低.
- 对于工业应用来说,开发用于小样本场景的强大的诊断方法至关重要.
研究的目的:
- 提出一个多传感器双层信息融合方法,用于在小样本条件下进行滚动轴承故障诊断.
- 通过有效利用多传感器数据,提高故障诊断的准确性和效率.
主要方法:
- 利用了由Aquila优化算法优化的变化模式分解 (VMD) 来分解轴承振动信号.
- 实施了双层融合策略:使用最大信息系数 (MIC) 和第二层信号重建的第一层特征融合.
- 将故障模式和严重程度识别集成到统一的诊断模型中.
主要成果:
- 与传统方法相比,拟议的方法在公共和定制数据集上实现了更高的诊断准确性.
- 证明了Aquila优化器在优化信号分解的VMD参数方面的有效性.
- 验证了用于轴承故障诊断的双层聚变方法的准确性,稳定性和效率.
结论:
- 多传感器双层信息融合方法在滚动轴承故障诊断方面取得了重大进展,特别是在有限的数据的情况下.
- 优化的VMD和基于MIC的特征融合的集成为准确的故障识别提供了一个强大的框架.
- 该方法为提高旋转机械状态监控可靠性的有前途的解决方案.
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