多轴承故障诊断方法基于卷积自编码器因果解域概括
Xinyang Cui1, Hongfei Zhan1, Kang Han1
1Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China.
ISA transactions
|May 14, 2025
概括
这项研究引入了一种用于多轴承故障诊断的新型因果脱网络,改善了转移学习模型的概括性. 该方法有效地诊断出故障,即使数据有限和条件不同.
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:
- 用于轴承故障诊断的电流转移学习 (TL) 通常忽略了多轴承系统.
- 挑战包括有限的故障样本和由于工作条件的变化而导致的分配转移.
- 现有的域泛化方法可能会过度简化数据生成,引入偏差.
研究的目的:
- 开发一种强大的转移学习方法,用于诊断多轴承系统的故障.
- 在域移动下增强故障诊断模型的概括能力.
- 准确诊断和定位故障使用辅助轴承的振动信号.
主要方法:
- 提出基于卷积自编码器 (CDN-CAE) 的因果解网络.
- 使用最大优化和相互信息神经估计来将时间序列数据解成因果和非因果因素.
- 引入因子分离的聚合损失和信息完整性和稳定性的重建损失.
主要成果:
- CDN-CAE方法有效地诊断和定位多轴承系统中的故障.
- 与现有方法相比,表现出更好的概括能力.
- 在实验验证中达到高精度和整体稳定性.
结论:
- 拟议的CDN-CAE方法为多轴承故障诊断提供了一个全面的方法.
- 成功地解决了现有的TL和域泛化技术的局限性.
- 为实际的工程场景提供了强大而准确的解决方案.
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