作者
Chuang Sun, Meng Ma, Zhibin Zhao, Shaohua Tian, Ruqiang Yan, Xuefeng Chen
发表日期
2019/4/1
期刊
IEEE Transactions on Industrial Informatics
卷号
15
期号
4
页码范围
2416-2425
出版商
IEEE
简介
Deep learning with ability to feature learning and nonlinear function approximation has shown its effectiveness for machine fault prediction. While, how to transfer a deep network trained by historical failure data for prediction of a new object is rarely researched. In this paper, a deep transfer learning (DTL) network based on sparse autoencoder (SAE) is presented. In the DTL method, three transfer strategies, that is, weight transfer, transfer learning of hidden feature, and weight update, are used to transfer an SAE trained by historical failure data to a new object. By these strategies, prediction of the new object without supervised information for training is achieved. Moreover, the learned features by deep transfer network for the new object share joint and similar characteristic to that of historical failure data, which is beneficial to accurate prediction. Case study on remaining useful life (RUL) prediction of cutting tool is …
学术搜索中的文章
C Sun, M Ma, Z Zhao, S Tian, R Yan, X Chen - IEEE transactions on industrial informatics, 2018