与数据增强和贝叶斯推断对不平衡轴承故障诊断的贝叶斯推理进行对比增强的对抗域概括网络
Rui Liu1, Jimeng Li1, Xilei Guan1
1College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, PR China; Hebei Key Laboratory of Measurement Technology and Instrumentation, Yanshan University, Qinhuangdao 066004, PR China.
ISA transactions
|November 18, 2025
概括
这项研究引入了一种用于滚动轴承失衡故障诊断的新框架,通过整合数据增强和贝叶斯推理来提高未见数据的准确性. 这种方法提高了工业设备健康监测的概括性.
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:
- 先进的故障诊断依赖于广泛的标记数据,但数据集经常表现出类不平衡和域转移.
- 未见的运行条件或设备变化会导致模型性能下降,因为先前知识不足.
研究的目的:
- 为滚动轴承的不平衡故障诊断提出一个对比增强的对抗领域概括框架.
- 解决工业设备健康监测中阶级不平衡和领域概括的挑战.
主要方法:
- 开发了一种以关联为导向的适应混合,用于类不平衡,以及具有多尺度卷积和注意力的特征提取器.
- 集成的域间对比损失,用于域不变表示的对抗训练.
- 采用贝叶斯融合机制,具有动态加权,用于准确识别未见域数据.
主要成果:
- 拟议的框架在使用滚动轴承数据集的交叉条件和交叉机器实验任务中实现了卓越的诊断准确性.
- 在不同和未见的场景下识别故障方面表现出强大的概括能力.
- 有效地缓解了类不平衡,并在多个源域中增强了功能学习.
结论:
- 对比增强的对抗域概括框架为不平衡故障诊断提供了可靠的解决方案.
- 数据增强和贝叶斯推理的整合显著提高了模型的稳定性和概括性.
- 这种方法为工业设备健康监测系统提供了一个有希望的方向.
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