• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability engineering & system safety >Remaining useful life estimation of bearings under different working conditions via Wasserstein distance-based weighted domain adaptation
【24h】

Remaining useful life estimation of bearings under different working conditions via Wasserstein distance-based weighted domain adaptation

机译：Remaining useful life estimation of bearings under different working conditions via Wasserstein distance-based weighted domain adaptation

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

摘要

Various transfer learning methods have been applied in the remaining useful life estimation of bearings to reduce the data distribution discrepancy under different working conditions. However, the transferability of the sample (i.e., the sample quality) is always ignored. Low-quality samples caused by noise and outliers inevitably exist in the industrial data, which may negatively affect feature extraction and alignment. This article proposes a Wasserstein distance-based weighted domain adversarial neural network to utilize sample quality which is measured by the domain classifier. The feature extractor tends to learn the representations from the samples with cross-domain similarity. Feature alignment is fine-tuned according to the sample weights. The effectiveness of the proposed method is validated using IEEE PHM Challenge 2012 dataset. The comparison results prove the features extracted from the proposed approach are more domain-invariant.

著录项

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