轴承故障诊断方法基于增强的VMD和适应性优化的SDAE
Xianlin Ren1,2, Haowen Li1, Laixian Chen1
1School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
PloS one
|December 4, 2025
概括
这项研究引入了一种新的方法,用于使用自适应变化模式分解和虫优化来诊断电机滚动轴承故障. 该方法在噪音条件下有效提取振动信号,确保高预测准确度和强大的性能.
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:
- 电机滚动轴承在工业生产中至关重要.
- 由于操作条件和噪音,振动信号提取和故障诊断具有挑战性.
- 现有的方法经常与复杂的噪音环境作斗争.
研究的目的:
- 提出一种适应变化模式分解 (VMD) 方法,该方法由虫算法优化,用于信号分解和特征提取.
- 为电机滚动轴承开发一个强大的故障诊断模型.
- 为了提高轴承故障诊断的准确性和抗噪能力.
主要方法:
- 根据虫优化算法进行信号分解的自适应性VMD.
- 复合优化指标函数 (塔尼莫托系数,顺序,库尔托斯) 作为健身函数.
- 复合多尺度变量对于特征提取.
- 增强的Sine Cosine算法 (SCA) 与考希混乱突变用于优化堆叠的自动编码器 (SDAE) 超参数.
- 使用CWRU开放轴承数据集进行验证.
主要成果:
- 提出的方法有效地分解和提取振动信号,即使在强大的噪音.
- 使用复合多尺度顺序 Entropy 成功创建了特征向量.
- 优化的SDAE模型实现了高预测准确度的故障诊断.
- 与其他现有方法相比,证明了更好的适应性和抗噪能力.
结论:
- 开发的故障诊断方法显示了对工业应用的重大前景.
- 虫优化与VMD的整合提供了一个灵活而强大的信号处理技术.
- 该方法为在具有挑战性的噪音条件下进行轴承故障诊断提供了可靠的解决方案.
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