滚动轴承的剩余使用寿命预测基于多尺度注意力剩余网络
Lin Song1,2, Jun Wu3, Liping Wang3
1School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
预测滚动轴承的剩余使用寿命 (RUL) 是至关重要的. 一个新的深度学习模型,FRMARNet,提高了从振动信号的RUL预测准确度,同时减少了参数.
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:
- 滚动轴承的剩余使用寿命 (RUL) 预测对于预测性维护至关重要.
- 信息理论等传统方法与复杂的振动信号作斗争.
- 深度学习,特别是卷积神经网络 (CNN),在RUL预测中显示了自动特征提取的前景.
研究的目的:
- 开发一种新的深度学习模型,用于准确预测滚动轴承的RUL.
- 解决现有的多尺度CNN的局限性,例如参数增加和信息歧视效率低下.
- 引入一种轻量级的架构,增强对振动信号的功能学习.
主要方法:
- 一个新的特征重用多尺度注意力剩余网络 (FRMARNet) 被开发出来.
- 为了自动选择信息,采用了跨道的最大聚合层.
- 一个轻量级的多尺度注意力单元被设计用于从振动信号中提取和重新校准降解信息.
主要成果:
- 与最先进的方法相比,FRMARNet模型实现了更高的RUL预测准确度.
- 拟议的模型显示了模型参数数量的减少.
- FRMARNet有效地建立了一个从振动信号到RUL的端到端映射.
结论:
- FRMARNet为滚动轴承中的RUL预测提供了一个有效的解决方案.
- 该模型的设计提高了分析复杂的振动数据的效率和准确性.
- 这种方法提升了旋转机械的预测性维护能力.
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