4.7 Article

Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds

期刊

JOURNAL OF MANUFACTURING SYSTEMS
卷 62, 期 -, 页码 186-198

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jmsy.2021.11.016

关键词

Unsupervised domain-share CNN; Fault transfer diagnosis; Time-varying speeds; Cauchy kernel-induced maximum mean difference; Adjustable and segmented factors

资金

  1. National Key Research and Development of China [2020YFB1712100]
  2. National Natural Science Foundation of China [51905160]
  3. Natural Science Fund for Excellent Young Scholars of Hunan Province [2021JJ20017]
  4. Fundamental Research Funds for the Central Universities [531118010335]

向作者/读者索取更多资源

This paper proposes an unsupervised domain-share convolutional neural network method for efficient fault transfer diagnosis of machines from steady speeds to time-varying speeds. By improving the efficiency and robustness of feature adaptation and simultaneously extracting domain invariant features from the source domain and target domain, the proposed method aims to improve diagnosis accuracy and transferability.
The existing deep transfer learning-based intelligent fault diagnosis studies for machinery mainly consider steady speed scenarios, and there exists a problem of low diagnosis efficiency. In order to overcome these limitations, an unsupervised domain-share convolutional neural network (CNN) is proposed for efficient fault transfer diagnosis of machines from steady speeds to time-varying speeds. First, a Cauchy kernel-induced maximum mean discrepancy based on unbiased estimation is developed for improving the efficiency and robustness of feature adaptation. Secondly, an unsupervised domain-share CNN is constructed to simultaneously extract the domain invariant features from the source domain and the target domain. Finally, adjustable and segmented balance factors are designed to flexibly weigh the distribution-adaptation loss and cross-entropy loss to improve diagnosis accuracy and transferability. The proposed method analyzes raw vibration signals collected from bearings and gears under different rotating speeds. Results of case studies show that the proposed method can achieve higher diagnosis accuracy, faster convergence, and better robustness than the reported methods, which demonstrates its potential applications in machine fault transfer diagnosis from a steady speed condition to a time-varying speed condition.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据