基于 SSA-VMD 和 RCMDEDE 的滚动轴承故障诊断方法
Xiangkun Wang1, JiaHong Li1, Zhe Jing1
1College of Mechanical Engineering, Beihua University, Jilin City, Jilin, 132021, China.
Scientific reports
|December 24, 2024
概括
这项研究引入了一种用于滚动轴承故障诊断的新方法,使用子搜索算法 (SSA) 优化的变化模式分解 (VMD) 和精制复合材料多尺度分散 (RCMDE). 这种方法提高了故障识别的准确性和稳定性,即使增加了噪音.
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:
- 滚动轴承故障诊断面临的挑战是弱特征提取和糟糕的泛化.
- 现有的方法很难有效地识别各种故障类型,并在噪音条件下保持性能.
研究的目的:
- 开发一个先进的故障诊断方法,用于滚动轴承.
- 为了提高轴承故障识别的准确性,概括性和稳定性.
主要方法:
- 一种结合Sparrow搜索算法 (SSA) 优化的变化模式分解 (VMD) 进行信号分解的新方法.
- 精制复合多尺度分散 (RCMDE) 用于从重建的信号中提取特征.
- K-means KNN (KKNN) 分类器用于故障分类,根据各种分解和特征提取技术进行验证.
主要成果:
- 拟议的SSA-VMD-RCMDE方法证明了单个和复合轴承故障的优越故障识别能力.
- 该方法表现出优异的防噪性能和强大的概括能力,即使在人工添加噪声的情况下也保持高精度.
- 对比分析证实了拟议方法相对于现有技术的有效性.
结论:
- 开发的SSA-VMD-RCMDE方法为滚动轴承故障诊断提供了强大而准确的解决方案.
- 这种方法为解决在不利条件下机械故障诊断方面的挑战提供了有价值的参考.
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