计算机科学
断层(地质)
感受野
卷积(计算机科学)
人工智能
方位(导航)
特征提取
模式识别(心理学)
控制重构
核(代数)
特征(语言学)
适应(眼睛)
控制理论(社会学)
人工神经网络
数学
地质学
嵌入式系统
控制(管理)
哲学
物理
地震学
光学
组合数学
语言学
作者
Qin Zhou,Zuqiang Su,Lanhui Liu,Xiaolin Hu,Jianhang Yu
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2022-01-07
卷期号:43 (1): 575-585
被引量:4
摘要
This study presents a fault diagnosis method for rolling bearing based on multi-scale deep subdomain adaptation network (MSDSAN). The proposed MSDSAN, as improvement of deep subdomain adaptation network (DSAN), is an unsupervised transfer learning method. MSDSAN reduces the subdomain distribution discrepancy between domains rather than marginal distribution discrepancy, and so better domain invariant fault features are derived to avoid misalignment between domains. Aiming at avoiding fault information loss by fixed receptive fields feature extraction, selective kernel convolution module is introduced into feature extraction of MSDSAN, by which multiple receptive fields are applied to ensure an optimal receptive field for each working condition. Moreover, contribution rates are adaptively assigned to all receptive fields, and the disturbing information extracted by inappropriate receptive fields is further eliminated. As a result, more comprehensive and effective fault information is derived for bearing fault diagnosis. Fault diagnosis experiment of bearings is performed to verify the superiority of the proposed method, and the experimental results demonstrate that MSDSAN achieves better transfer effects and higher accuracy than SOTA methods under varying working conditions.
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