一种基于WOA-VMD和GAT的滚动轴承故障诊断方法
Yaping Wang1,2, Sheng Zhang2, Ruofan Cao2
1Key Laboratory of Advanced Manufacturing and Intelligent Technology, Harbin University of Science and Technology, Harbin 150080, China.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究引入了一种新的方法来诊断滚动轴承故障,通过使用鱼优化算法-变化模式分解 (WOA-VMD) 和图形注意网络 (GAT) 来降低噪声. 该技术在识别各种轴承故障方面达到100%的准确性,显著提高了诊断可靠性.
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:
- 工业环境产生来自滚动轴承的噪音振动信号,妨碍了准确的故障诊断.
- 现有的信号分解方法受到最终效应和模式混合的影响,影响诊断精度.
- 有效的降噪对于可靠的滚动轴承故障检测至关重要.
研究的目的:
- 提出一个强大的滚动轴承故障诊断方法,克服噪声干扰和信号分解的挑战.
- 通过将优化算法与先进的信号处理和机器学习技术相结合,提高诊断准确度.
- 验证拟议方法在降噪和故障分类方面的有效性.
主要方法:
- 采用鱼优化算法 (WOA) 来优化变量模式分解 (VMD) 参数 (惩罚因子和分解层).
- 利用皮尔森相关系数来选择相关的内在模式函数 (IMF) 组件用于信号重建和消除噪声.
- 开发了一个图表注意力网络 (GAT) 模型,将K-最近邻居 (KNN) 纳入图表构建和多头注意力用于故障分类.
主要成果:
- 显著降低噪音,特别是在振动信号的高频组件.
- 在滚动轴承故障诊断测试套件上实现了100%的准确率.
- 在诊断准确度方面表现优于其他四种比较方法,所有故障类型都被正确分类.
结论:
- 提出的基于WOA-VMD和GAT的方法有效地抑制噪声,并准确诊断滚动轴承故障.
- 适应性VMD优化和GAT为复杂的工业条件监测提供了强大的框架.
- 这种方法提供了一个高度可靠的解决方案,以提高机械维护的安全性和效率.
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