在可变工作条件下对极其有限的标记样本进行计量学习引导的半监督路径相互作用故障诊断方法
Zheng Yang1, Fei Chen2, Binbin Xu2
1School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
智能故障诊断由Tri-CLAN网络改进,该网络使用有限的标记数据和未标记数据学习独立于工作条件的表示. 这种方法提高了对工业应用的概括性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 智能故障诊断面临挑战,原因是有限的标记数据和不同的操作条件.
- 现有的方法在不同的工作环境中难以适应.
研究的目的:
- 提出一种新型网络,即三重引导路径交互梯子网络 (Tri-CLAN),用于强大的智能故障诊断.
- 从有限的标记数据和丰富的未标记数据中开发一种学习分布不变表示的方法.
主要方法:
- 使用了带有路径交互的编码器-解码器结构,并简化了CNN架构.
- 集成了一个元素添加组合激活功能,以提高网络效率.
- 在功能空间中引入了度量学习,特别是三倍损失,以改进样本歧视.
主要成果:
- 三CLAN网络展示了有效利用没有标签的数据与更少的参数.
- 度量学习使硬样本的挖掘和学习工作条件独立的表示成为可能.
- 实验结果验证了所提出的三CLAN模型的概括性和适用性.
结论:
- 在数据稀缺和可变条件下,Tri-CLAN为智能故障诊断提供了一个有前途的解决方案.
- 网络架构和指标学习的结合显著提高了诊断性能.
- 这种方法有助于开发更具适应性和可靠性的工业诊断系统.
相关概念视频
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