一个基于多对手域适应的电机轴承故障诊断模型
Xin-Ming Liu1, Rui-Ming Zhang1, Jin-Ping Li2
1Liaoning Technical University, Huludao, 125105, China.
Scientific reports
|November 23, 2024
概括
本研究引入了一种新的电机轴承故障诊断模型,使用多对手域调整来克服数据分布差异. 该模型在跨领域转移实验中取得了出色的诊断结果.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 跨领域传输学习对于故障诊断至关重要,但由于不同设备的数据分布差异而受到阻碍.
- 现有的方法在不同数据集的特征转移和参数对齐方面存在困难.
研究的目的:
- 开发一个强大的电机轴承故障诊断模型,有效地处理跨域数据分布的变化.
- 改进功能转移和参数对齐,用于工业设备诊断的域调整.
主要方法:
- 一个改进的剩余网络,用于增强特征提取.
- 选择性内核网络 (SKNet) 和全球上下文网络 (GCNet) 的整合,用于注意力机制和全球信息利用.
- 多个内核最大平均差异与动态调整因子和多个域区分器,以实现强大的域适应.
主要成果:
- 拟议的模型在不同操作条件和设备的转移实验中表现出卓越的性能.
- 实现了边际和条件分布的有效调制,提高了诊断准确度.
- 该模型成功地解决了在轴承故障诊断中数据分布差异的挑战.
结论:
- 多对手域适应模型为在不同数据分布下对电机轴承故障诊断提供了强大的解决方案.
- 集成先进的网络架构和域调整技术带来了出色的诊断结果.
- 这种方法在工业机械智能故障诊断领域取得了重大进展.
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