一种滚动轴承故障诊断方法,基于野马优化器增强的VMD和改进的GoogleLeNet
Xiaoliang He1,2, Feng Zhao3, Nianyun Song4
1School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
这项研究引入了一个新的故障诊断方法,用于早期阶段的振动信号. 它在非静止条件下准确检测故障,即使在噪声下也能达到高精度.
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:
- 在机械设备的早期故障检测对于防止灾难性故障至关重要.
- 振动信号往往表现出弱故障特征和强大的非静止性,使分析复杂化.
- 传统的方法难以应对早期故障信号的复杂性.
研究的目的:
- 开发一个强大的故障诊断方法,用于早期阶段的振动信号.
- 增强从非静止数据中提取弱故障特征.
- 提高机器故障诊断的准确性和可靠性.
主要方法:
- 增强的变化模式分解 (VMD) 通过改进的野生马优化器 (IWHO) 和帐混乱映射进行优化.
- 自动优化VMD参数 (K和α) 以实现精确的信号分解.
- 从基于kurtosis的选定的内在模式函数 (IMF) 中提取时间频率特征.
- 使用结构修改的GoogleLeNet进行分类,具有级联卷曲和TReLU激活.
主要成果:
- 拟议的方法准确地分解非静止的振动信号.
- 弱故障特征被有效地提取并用于诊断.
- 在滚动轴承数据集上的四种故障类型中实现了99.17%的准确性.
- 在噪音条件下保持了超过95.80%的准确性.
结论:
- 结合VMD-GoogLeNet的方法为早期故障诊断提供了一个强大的解决方案.
- 该方法在处理非静止和噪音振动数据方面表现出高效率.
- 这种技术显著提高了状态监控系统的可靠性.
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