计算机科学
编码器
断层(地质)
领域(数学分析)
人工智能
自编码
交叉验证
领域(数学)
可靠性(半导体)
特征提取
特征(语言学)
模式识别(心理学)
数据挖掘
机器学习
算法
深度学习
数学
功率(物理)
语言学
物理
哲学
量子力学
地震学
地质学
操作系统
数学分析
纯数学
作者
Shi-Zheng Yuan,Zhaohua Liu,Hua‐Liang Wei,Lei Chen,Mingyang Lv,Xiao-Hua Li
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:73: 1-11
标识
DOI:10.1109/tim.2023.3331436
摘要
In recent years, most existing domain-adapted bearing fault diagnoses for rotating machinery are designed to decrease domain drifts for various operating conditions with an assumption that sufficient tag data are available. To overcome data scarcity, a possible solution is to use fault information of other machines of the same category to diagnose the status of a target machine (i.e., cross-machine diagnosis). This paper proposes a variational auto-encoder based multi-source deep domain adaptation model using optimal transport for cross-machine fault diagnosis of rotating machinery (named MDVAEOT). This is fundamentally different from most diagnostic models where both train and test data belong to the same machine. Firstly, it uses unlabeled samples of the machines to be diagnosed to establish the target dataset and faulty samples of machines of the same category (containing labels) to form the source dataset. Additionally, the method performs feature extraction on the dataset using variational auto-encoder networks and improves the reliability of extracted data features by the approximation of fixed probability. Finally, to shrink cross-machine differences between the two domains, we introduce optimal transport (OT) theory. OT distance is used to shares fault-related features between the two domains mentioned above to complete the cross-machine diagnosis task. Better accuracy and timeliness are offered by this proposed means compared to other existing intelligent methods in this field.
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