基于动态时间变化的响应和联合注意力机制的可变速度的燃气轮机滚动轴承的故障诊断方法
Hongxun Lv1, Zhilin Dong2, Xueyi Li3
1College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究引入了一种新的多通道变速注意力框架 (MC-VSAttn) 用于稳固的燃气轮机滚动轴承故障诊断. 该方法即使在变速的复杂,非静止条件下也能准确识别故障.
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:
- 燃气轮机滚动轴承由于可变的操作条件,如频繁的启动/停止和转速变化而面临显著的非静止性.
- 这种非静止性对这些关键组件的准确故障诊断构成了重大挑战.
- 现有的故障诊断方法与轴承振动信号的复杂,时间变化的性质作斗争.
研究的目的:
- 为在非静止和变速条件下运行的燃气轮机滚动轴承开发一个强大的故障诊断框架.
- 通过明确建模时间变化的信号特征来提高故障识别的准确性和稳定性.
- 通过整合多道融合和分层注意力机制来改进现有方法.
主要方法:
- 提出了一个多道变速注意力框架 (MC-VSAttn).
- 构建多通道输入以获取全面的故障信息.
- 引入了一个动态时间变化的响应模块,以自适应地建模非静止特征.
- 联合道和空间联合注意力机制,用于选择性信息增强.
主要成果:
- 在来自清华大学和华中科技大学的变速数据集上,MC-VSAttn实现了99.14%和98.23%的高准确率.
- 该框架在复杂的操作条件下展示了强大的故障识别能力.
- 废弃实验证实了动态时间变量响应模块和联合注意力机制对性能的重大贡献.
结论:
- 拟议的MC-VSAttn框架有效地解决了气轮机滚动轴承中非静止振动信号的挑战.
- 多通道融合,动态时间变化的建模和共同关注的整合,带来了卓越的故障诊断准确性和稳定性.
- 这种方法为变速机械的可靠状态监测和故障诊断提供了有希望的解决方案.
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