计算机科学
学习迁移
人工智能
域适应
领域(数学分析)
断层(地质)
深度学习
一般化
机器学习
数据建模
人工神经网络
领域知识
适应(眼睛)
模式识别(心理学)
数据挖掘
数学
数据库
地震学
数学分析
地质学
物理
光学
分类器(UML)
作者
Chenyu Liu,Konstantinos Gryllias
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:18 (9): 5760-5770
被引量:39
标识
DOI:10.1109/tii.2021.3103412
摘要
State-of-the-art deep learning models remain data-intensive, requiring large training datasets to ensure their generalization ability. However, in industry, it is quite expensive or impractical to obtain massive training samples for condition monitoring practitioners. This article proposes a simulation-driven domain adaptation method to circumvent the data deficiency issue using physical-based simulations. A bearing phenomenological model is developed to generate simulated vibration signals. In the frame of domain adaptation transfer learning, a domain adversarial neural network (DANN) is proposed utilizing the simulated data as the source domain. The DANN can align the coarse supervised source domain data and the fine supervised target domain data to conduct adversarial training. Experimental results indicate that the proposed method can reach high classification accuracy using a small amount of real data. Compared to nonadapted and other transfer learning models, the proposed method demonstrates superior performance for bearing fault diagnosis, which is very promising for real industrial applications.
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