领域对抗转移学习 承载故障诊断模型 纳入结构调整模块
Zhidan Zhong1, Hao Xie1, Zhenxin Wang1
1School of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了一种先进的轴承故障诊断模型,使用对抗性域调整和结构调整. 该模型有效地诊断工业环境中的复杂故障,提高设备可靠性.
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:
- 轴承故障诊断对于机械设备的可靠性至关重要.
- 传统方法在复杂的故障和手动超参数调整方面扎.
研究的目的:
- 为工业设备开发智能轴承故障诊断模型.
- 解决复杂故障诊断和超参数优化中传统方法的局限性.
主要方法:
- 提出了一个具有结构调整模块的域对抗性迁移学习模型.
- 对抗性域调整将预训练模型转移到目标数据集.
- 奥普图纳优化框架动态调整了网络架构和超参数.
主要成果:
- 该模型在诊断各种轴承故障类型时实现了高精度.
- 在复杂的工业环境中表现出强大的适应性和稳定性.
- 成功应对复杂和可变的操作条件所带来的挑战.
结论:
- 拟议的模型为智能设备故障诊断提供了有效的解决方案.
- 突出了迁移学习和工业应用的自动化优化潜力.
- 通过精确的故障检测提高机械设备的稳定性和可靠性.
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