SSG-Net:一个多分支的故障诊断方法,用于滚动压缩机使用旋转变压器滑窗,浅 ResNet,和全球注意力机制 (GAM) 的滚动压缩机
Zhiwei Xu1, Tao Liu1, Zezhou Xia1
1School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730070, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究介绍了SSG-Net,这是一种新的深度学习方法,用于准确的滚动压缩机故障诊断. 该方法通过提高故障识别精度,提高旋转机械和制冷系统的可靠性.
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:
- 卷轴压缩机对于旋转机械和制冷至关重要,需要可靠的故障诊断.
- 现有的方法与不同的操作状态,故障模式和条件作斗争.
研究的目的:
- 为滚动压缩机开发一种高效准确的故障诊断方法.
- 为了增强复杂故障环境的特征提取和识别能力.
主要方法:
- 一个多分支卷积神经网络 (SSG-Net),集成了Swin变压器,全球注意力机制 (GAM) 和ResNet.
- 通过短时间里埃转换将1D时间序列信号转换为2D图像.
- 使用注意力机制和卷积网络的全球和本地特征的组合提取.
主要成果:
- 在滚动压缩机数据集上实现了97.44%的诊断准确度.
- 在CWRU数据集上证明了99.78%的诊断准确性.
- 在识别精度和收速度方面表现优于现有的比较模型.
结论:
- SSG-Net为滚动压缩机故障诊断提供了卓越的性能.
- 该方法在各种操作条件下显示出稳定性和高精度.
- 这种深度学习方法显著提高了关键机器的诊断能力.
相关概念视频
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