滚动轴承的故障诊断方法基于自适应修改的CEEMD和1DCNN模型
Shuzhi Gao1, Tianchi Li2, Yimin Zhang2
1Equipment Reliability Institute, Shenyang University of Chemical Technology, Shenyang 110142, China.
ISA transactions
|June 23, 2023
概括
这项研究引入了一种新的方法,通过减少振动信号中的噪声来诊断滚动轴承故障. 适应性修改的互补组合实证模式分解 (AMCEEMD) 和1DCNN模型在轴承故障诊断中实现了更高的分类准确性.
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:
- 滚动轴承故障诊断是具有挑战性的,因为复杂的工作环境和的振动信号.
- 有效的除,特征提取和分类对于准确的故障检测至关重要.
研究的目的:
- 提出一种新的滚动轴承故障诊断模型.
- 通过改进信号处理和特征提取来提高故障分类的准确性.
主要方法:
- 开发了一种自适应修改的互补集体实证模式分解 (AMCEEMD) 方法,通过模糊和曲解来增强CEEMD,以减少噪音和冲动识别.
- 使用的能量比率,模糊和库尔托斯对于内在模式函数 (IMF) 的自适应选择.
- 输入选定的IMF特征进入一个一维卷积神经网络 (1DCNN) 用于故障分类.
主要成果:
- 在AMCEEMD方法有效地消除噪音和识别冲动信号,产生标准的IMFs.
- 适应性IMF选择策略改善了特征表示.
- 拟议的AMCEEMD-1DCNN模型在实验验证中显示出与其他方法相比,更高的分类准确性.
结论:
- AMCEEMD-1DCNN模型为滚动轴承故障诊断提供了一个有效的解决方案.
- 集成先进的信号处理和深度学习显著改善了在噪音条件下诊断性能.
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