因果解 域名 时间序列信号故障诊断的泛化
Linshan Jia1, Tommy W S Chow1, Yixuan Yuan2
1Department of Electrical Engineering, City University of Hong Kong, 999077, Hong Kong Special Administrative Region of China.
概括
本研究介绍了因果解域泛化 (CDDG) 用于在没有目标域数据的情况下进行故障诊断. 在时间序列数据中,CDDG有效地将因果因素和域因素分开,改善了跨不同条件的概括性.
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:
- 基于域泛化的故障诊断 (DGFD) 旨在识别无法访问目标域数据的故障.
- 现有的DGFD方法通常依赖于表面的统计建模,并且仅限于振动数据集.
研究的目的:
- 提出一种新的DGFD方法,即因果解域泛化 (CDDG),可以重新建立数据生成过程.
- 通过在时间序列数据中解脱因果和域特定因素来解决现有方法的局限性.
主要方法:
- CDDG使用结构性因果模型将时间序列数据分解成与故障相关 (因果) 和与域相关 (非因果) 的表示.
- 关键组件包括因果聚合损失,重建损失和冗余减少损失,以实现强大的特征学习.
主要成果:
- 在5个跨机器振动故障诊断案例和3个跨环境声学异常检测案例中,CDDG表现出卓越的性能.
- 该方法在概括能力方面表现优于八种最先进的DGFD方法.
结论:
- 通过使用因果推理,CDDG可以更深入地了解DGFD的数据生成过程.
- 该研究验证了DGFD超越振动数据的广泛适用性,并为未来的研究引入了一个基准.
相关概念视频
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Using Kirchhoff's Voltage Law (KVL) to analyze this circuit helps determine the total asymmetrical fault current, which consists...
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