...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Measurement Science & Technology >Adversarial domain adaptation with classifier alignment for cross-domain intelligent fault diagnosis of multiple source domains
【24h】

Adversarial domain adaptation with classifier alignment for cross-domain intelligent fault diagnosis of multiple source domains

机译：对跨域智能故障诊断的分类器对齐的对手域适应多个源极域的跨域对齐

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recently, most cross-domain fault diagnosis methods focus on single source domain adaptation. However, it is usually possible to obtain multiple labeled source domains in real industrial scenarios. The question of how to use multiple source domains to extract common domain-invariant features and obtain satisfactory diagnosis results is a difficult one. This paper proposes a novel adversarial domain adaptation with a classifier alignment method (ADACL) to address the issue of multiple source domain adaptation. The main elements of ADACL consist of a universal feature extractor, multiple classifiers and a domain discriminator. The parameters of the main elements are simultaneously updated via a cross-entropy loss, a domain distribution alignment loss and a domain classifier alignment loss. Under the framework of multiple loss cooperative learning, not only is the distribution discrepancy among all domains minimized, but so is the prediction discrepancy of target domain data among all classifiers. Two experimental cases on two source domains and three source domains verify that the ADACL can remarkably enhance the cross-domain diagnostic performance under diverse operating conditions. In addition, the diagnostic performance of different methods is extensively evaluated under noisy environments with a different signal-to-noise ratio.
机译：None

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号