Residual deep subdomain adaptation network: A new method for intelligent fault diagnosis of bearings across multiple domains

2022 ◽  
Vol 169 ◽  
pp. 104635
Zuoyi Chen ◽  
Jun Wu ◽  
Chao Deng ◽  
Chao Wang ◽  
Yuanhang Wang
2021 ◽  
Vol 427 ◽  
pp. 96-109
Nannan Lu ◽  
Hanhan Xiao ◽  
Yanjing Sun ◽  
Min Han ◽  
Yanfen Wang

Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

2021 ◽  
Vol 1966 (1) ◽  
pp. 012031
Zikou Yu ◽  
Yongyong Duan ◽  
Zongling Wu ◽  
Yuhang Wang

Sign in / Sign up

Export Citation Format

Share Document