计算机科学
人工智能
学习迁移
特征学习
断层(地质)
深度学习
特征(语言学)
嵌入
对抗制
领域(数学分析)
机器学习
数据挖掘
数学
地质学
数学分析
哲学
地震学
语言学
作者
Ming Zhang,Duo Wang,Weining Lu,Jun Yang,Zhiheng Li,Bin Liang
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2019-01-01
卷期号:7: 65303-65318
被引量:98
标识
DOI:10.1109/access.2019.2916935
摘要
In recent years, intelligent fault diagnosis technology with the deep learning algorithm has been widely used in the manufacturing industry for substituting time-consuming human analysis method to enhance the efficiency of fault diagnosis. The rolling bearing as the connection between the rotor and support is the crucial component in rotating equipment. However, the working condition of the rolling bearing is under changing with complex operation demand, which will significantly degrade the performance of the intelligent fault diagnosis method. In this paper, a new deep transfer model based on Wasserstein distance guided multi-adversarial networks (WDMAN) is proposed to address this problem. The WDMAN model exploits complex feature space structures to enable the transfer of different data distributions based on multiple domain critic networks. The essence of our method is learning the shared feature representation by minimizing the Wasserstein distance between the source domain and target domain distribution in an adversarial training way. The experiment results demonstrate that our model outperforms the state-of-the-art methods on rolling bearing fault diagnosis under different working conditions. The t-distributed stochastic neighbor embedding (t-SNE) technology is used to visualize the learned domain invariant feature and investigate the transferability behind the great performance of our proposed model.
科研通智能强力驱动
Strongly Powered by AbleSci AI