基于数字双胞胎数据和改进的Conv的滚动轴承故障诊断研究Next
Chao Zhang1,2, Feifan Qin1,2, Wentao Zhao1,2
1College of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究提出了一个新的框架,用于使用数字双胞胎和深度学习来诊断滚动轴承故障. 该方法提高了故障检测的准确性和稳定性,克服了机械设备的数据限制.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 滚动轴承故障诊断面临挑战,因为实际故障数据有限,准确性低.
- 现有的方法在数据稀缺和在旋转机械中实现可靠的故障检测方面扎.
研究的目的:
- 引入一种用于诊断滚动轴承故障的新型框架.
- 为了应对有限的故障数据密度和当前方法的不充分准确性所带来的挑战.
- 改进旋转机械设备故障的检测和分类.
主要方法:
- 利用数字双胞胎模型生成大量,平衡的模拟数据集.
- 增强了ConvNext深度学习网络,使用相似性注意模块 (SimAM) 和高效通道注意网络 (ECA) 来进行特征提取.
- 员工转移学习以训练源域数据的增强模型,并将其应用于故障诊断的目标域轴承.
主要成果:
- 拟议的框架有效地产生大规模的模拟数据集,减轻低故障数据密度的问题.
- 增强的ConvNext网络与SimAM和ECA模块改善了特征提取能力.
- 转移学习使准确的故障诊断成为可能,与现有方法相比,显示出更好的准确性和稳定性.
结论:
- 这种新的框架通过利用数字双胞胎数据和先进的深度学习成功诊断滚动轴承故障.
- 该方法为机械设备故障检测提供了强大的解决方案,显著提高了准确性.
- 这种方法为现实应用程序提供了可行的替代方案,因为故障数据很少.
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