过度拟合
一般化
计算机科学
人工智能
机器学习
一致性(知识库)
断层(地质)
不变(物理)
领域知识
数据挖掘
人工神经网络
数学
地质学
数学分析
地震学
数学物理
作者
Jie Li,Yu Wang,Zi Ye,Haijun Zhang,Chen Li
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:19 (4): 5915-5924
被引量:2
标识
DOI:10.1109/tii.2022.3174711
摘要
Due to the lack of faulty data on the target machine, intelligent networks often need to learn fault knowledge from other relevant machines. Unfortunately, data from different machines introduce individualized deviations, which may lead to overfitting of network learning and reduce its generalization ability. To address the problem, this article proposes a collaborative multimachine generalization method—causal consistency network (CCN), which mines the invariant causal information in individualized machines through a collaborative way to achieve knowledge generalization. In CCN, instead of emphasizing the domain invariance of features, causal consistency loss depicting the consistency of fault causality representations in deep latent variables is proposed. Moreover, to transform the individualized data of different machines into consistent representations, a collaborative training loss is proposed to describe the underlying invariant causal mechanism of the fault features. The generalization results among 6 machines containing 43 individual bearings and 20 operating conditions demonstrate the superiority of CCN.
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