注意引导的剩余时空网络与标签规范化用于小样本故障诊断
Yanlong Xu1, Liming Zhang1,2, Ling Chen1
1School of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了用于旋转机械故障诊断的先进深度学习模型. 这种新的方法提高了诊断的准确性和稳定性,即使在有限的工业数据和不断变化的条件下.
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:
- 旋转机械的维护在很大程度上依赖于准确的故障诊断.
- 深度学习提供了有前途的智能诊断功能,但面临着小型数据集和可变操作条件的挑战.
研究的目的:
- 开发一个有效的深度学习模型,用于旋转机械故障诊断.
- 解决工业数据中小样本大小和不同工作条件的局限性.
主要方法:
- 开发了一个与注意力机制 (通道和空间注意力) 集成的残余卷积神经网络 (CNN).
- 该模型包含残余模块以减轻消失梯度和长短期记忆 (LSTM) 以实现时空特征融合.
- 使用标签平滑规范化 (LSR) 来管理标签样本中的分布差异.
主要成果:
- 拟议的方法证明了安全注射和Case Western Reserve University (CWRU) 数据集的振动信号数据的卓越诊断准确性.
- 该模型在不同的工作条件下表现出强大的强度.
结论:
- 基于注意力的剩余CNN与LSTM和LSR是旋转机械故障诊断的高效技术.
- 这种方法为现实世界的工业应用提供了强大的解决方案,数据有限.
相关概念视频
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