基于优化的变化模式分解和卷积神经网络的风力轮机轴承故障分类识别-双向封闭反复单元-注意力
Minan Tang1, Zhanglong Tao1, Changyou Wang2
1College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou, China.
Science progress
|February 16, 2026
概括
本研究引入了优化的变化模式分解 (VMD) 与CNN-BiGRU-Attention模型相结合,用于准确的风力轮机变速箱滚动轴承故障分类,提高发电机的可靠性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承是风力轮发电机的关键部件,直接影响性能和可靠性.
- 这些轴承的早期故障检测对于提高运行效率和防止故障至关重要.
研究的目的:
- 开发一种先进的方法来对风力轮机变速箱滚动轴承的故障进行分类.
- 提高风力轮机故障诊断系统的准确性和稳定性.
主要方法:
- 优化变量模式分解 (VMD) 使用改进的RIEM算法来选择最佳内在模式函数 (IMF).
- 使用复合多尺度斜率的特征提取用于多维信号分析.
- 实现一个卷积神经网络-双向门循环单元 (CNN-BiGRU) -注意力模型的故障分类.
主要成果:
- 拟议的CNN-BiGRU-Attention模型在分类滚动轴承故障方面表现出显著的有效性和稳定的性能.
- 优化的VMD成功地确定了最适合用于故障信号分析的IMF组件.
- 复合多尺度斜率 entropy 有效地捕获了复杂的故障信号特征.
结论:
- 优化VMD和CNN-BiGRU-Attention的综合方法为风力轮机滚动轴承故障诊断提供了一个强大的解决方案.
- 这项研究提供了新的技术见解,通过先进的故障检测来提高风力轮发电机的可靠性和效率.
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