深度多级特征融合网络,双重关注滚动轴承的剩余使用寿命预测
Yingming Yang1,2, Zhihai Wang3,4, Xiaoqin Liu1,2
1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, China.
Scientific reports
|April 28, 2025
概括
这项研究引入了一个深度的多级特征融合网络,对预测滚动轴承剩余使用寿命 (RUL) 进行了双重关注. 这种新的方法通过有效捕获关键降解信息来提高准确性和概括性.
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:
- 现有的滚动轴承寿命预测方法缺乏足够的降解信息挖掘能力.
- 不足够的关键时间步骤信息导致关键降解数据的丢失,影响预测准确性和概括性.
- 需要先进的模型来提高剩余使用寿命 (RUL) 预测的准确性和稳定性.
研究的目的:
- 提出一个新的深度多尺度特征融合网络,双重关注滚动轴承RUL预测.
- 为了提高降解信息的挖掘,并捕获关键的时间特征.
- 提高滚动轴承预测寿命模型的预测准确度和概括能力.
主要方法:
- 从滚动轴承的振动信号中获取多域特征集.
- 使用Squeeze-and-Excitation (SE) 注意力用于时间意义校准和关键信息捕获.
- 使用残余金字塔层构建一个深度多尺度特征提取和融合模块.
- 在双重注意力增强的变压器编码器的多头注意力机制中整合相对位置编码.
- 通过回归层映射高层特征,用于最终的RUL预测.
主要成果:
- 拟议的双重注意力增强型变压器网络显著提高了预测准确性.
- 该模型展示了用于滚动轴承寿命预测的增强泛化能力.
- 实验结果验证了在捕获降解信息方面比现有方法更优越的性能.
结论:
- 开发的双重重视的深度多级特征融合网络为滚动轴承RUL预测提供了强大而准确的框架.
- 该方法有效地解决了当前生命预测技术的局限性,通过改进信息挖掘和特征提取.
- 这种方法在预测滚动轴承的剩余使用寿命方面取得了重大进展.
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