轴承故障诊断基于高效的跨空间多尺度CNN变压器并行性
Qi Chen1, Feng Zhang2, Yin Wang1
1College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, 361021, China.
Scientific reports
|April 10, 2025
概括
本研究介绍了一种高效的跨空间多尺度CNN变压器并行 (ECMCTP) 模型,用于风力轮机轴承故障诊断. 该ECMCTP模型有效地提取时空特征,实现100%的准确性和在噪音条件下强大的性能.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 风力轮机轴承故障诊断对于运行安全和可靠性至关重要.
- 传统的串行深度学习模型在杂的环境中与时空特征提取作斗争,导致信息丢失.
研究的目的:
- 提出一个高效的跨空间多尺度CNN变压器并行 (ECMCTP) 模型,用于增强风力轮机轴承故障诊断.
- 改进从振动信号中提取时空特征,特别是在噪音条件下.
主要方法:
- 1D振动信号通过连续波纹转换 (CWT) 转换为2D时间频率图像.
- 平行CNN和变压器分支提取特征:CNN使用多尺度模块,反向残余结构 (RRS) 和高效多尺度注意力 (EMA);变压器使用双向门式反复单位 (BiGRU) 和变压器.
- 功能通过软max分类器连接和分类.
主要成果:
- 在无噪声条件下,ECMCTP模型在故障诊断中实现了100%的准确性.
- 该模型在低信号噪声比 (SNR) 条件下表现出卓越的稳定性和概括能力.
结论:
- 拟议的ECMCTP模型有效地解决了风力轮轴承故障诊断传统方法的局限性.
- 平行CNN-Transformer架构提供了卓越的稳定性和通用性,特别是在具有挑战性的杂环境中.
相关概念视频
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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