剩余收缩ViT与歧视性再平衡策略用于小型和不平衡的故障诊断
Li Zhang1, Shixing Gu1, Hao Luo1
1College of Information, Liaoning University, Shenyang 110036, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究引入了一个新的诊断框架,DSADRSViT-IIRL,以应对小,不平衡的数据集中的故障检测挑战. 该方法有效地处理轴承数据集中的类失衡和有限的样本大小.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 小型和不平衡的数据集对准确的故障诊断构成重大挑战.
- 有限的样本大小和健康样本比有缺陷的样本的普遍性阻碍了模型的性能.
- 现有的方法难以有效地解决有限的数据和严重的阶级不平衡.
研究的目的:
- 提出一个新的诊断框架,DSADRSViT-IIRL,专门为小型和不平衡的数据集设计.
- 在数据有限,类分布偏的场景中增强故障检测能力.
- 在现实应用中提高诊断模型的稳定性和准确性.
主要方法:
- 在视觉变压器 (ViT) 架构中整合双流自适应深剩余收缩区块 (DSA-DRSB).
- 在培训期间开发一个类间 - 类内再平衡损失 (IIRL) 以管理类不平衡.
- 适应性去除冗余信号信息,以专注于关键故障特征.
主要成果:
- 拟议的DSA-DRSB可自适应地处理输入数据,加强对全球受感场和局部故障特征的关注.
- 通过在培训期间将样本贡献脱而出,IIRL促进了稳定的模型趋同.
- 实验验证实验室和CWRU轴承数据集的实验验证表明在处理类不平衡方面表现优越.
结论:
- DSADRSViT-IIRL框架有效地解决了有限样本和轴承故障诊断中的类不平衡的双重挑战.
- 整合DSA-DRSB和IIRL显著提高了不平衡数据集的诊断准确性.
- 提出的方法为在实际工程场景中可靠检测故障提供了一个有希望的解决方案.
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