FDBRP:一个数据模型协同优化框架,以实现更高精度的轴承RUL预测
Muyu Lin1,2, Qing Ye1, Shiyue Na1,2
1School of Computer Science, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了一种新的特征融合和扩展因果卷积模型,用于轴承剩余使用寿命 (RUL) 预测 (FDBRP). FDBRP模型显著提高了滚动轴承的RUL预测准确度.
科学领域:
- 工程 工程师 工程师 工程师
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 预测剩余使用寿命 (RUL) 对于有效维护滚动轴承至关重要.
- 现有的方法经常与复杂的时空依赖性和数据限制作斗争.
研究的目的:
- 提出一个综合框架,FDBRP,用于准确预测滚动轴承的RUL.
- 在RUL预测模型中增强信号表示,时空特征一致性和远程依赖性捕获.
主要方法:
- 一个数据增强模块,具有滑动窗处理和2D功能连接.
- 一个功能融合模块,结合了图形卷积网络,多尺度时间卷积和注意力机制.
- 一个优化的扩展卷积模块,具有间隔采样和剩余连接.
主要成果:
- 拟议的FDBRP模型获得了0.756564.4的高平均得分.
- 在测试轴承的RUL预测中显示了较低的平均误差10.903656.
- 在RUL预测准确度方面表现优于最先进的基准方法.
结论:
- FDBRP模型为滚动轴承提供了卓越的RUL预测能力.
- 综合方法有效地解决了时空建模和远程依赖性捕获方面的挑战.
- 该方法显示了在预测性维护中的工业应用的巨大潜力.
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