一个改进的多尺度分支卷积神经网络用于滚动轴承故障诊断
Meng Xu1, Yaowei Shi1, Minqiang Deng1
1National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing, China.
PloS one
|September 13, 2023
概括
这项研究引入了一种改进的多尺度分支卷积神经网络,用于诊断滚动轴承故障. 该方法提高了复杂,杂的振动信号的准确性和效率.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 实际工程振动信号往往是复杂和杂的,具有挑战性的故障诊断.
- 工业应用要求诊断模型的高精度和计算效率.
研究的目的:
- 用复杂和杂的振动信号来解决滚动轴承故障诊断方面的挑战.
- 开发一种具有更高准确度和计算效率的诊断模型.
主要方法:
- 提出了一个改进的多级分支卷积神经网络 (CNN).
- 多级特征学习被用来从振动信号中提取故障信息.
- 整合了轻量级的动态可分离卷积,以减少模型大小并增强适应性学习.
主要成果:
- 拟议的方法在现有的多尺度神经网络上显示了显著的改进.
- 从复杂的振动数据中提取故障信息.
- 观察到减少计算损失和改进适应性学习能力.
结论:
- 改进的多尺度分支CNN是有效的滚动轴承故障诊断.
- 该方法为工业环境中准确有效的故障诊断提供了一个有前途的解决方案.
- 集成轻量级的动态可分离卷积增强了模型性能和效率.
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