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

2022 ◽  
Vol 169 ◽  
pp. 104635
Author(s):  
Zuoyi Chen ◽  
Jun Wu ◽  
Chao Deng ◽  
Chao Wang ◽  
Yuanhang Wang
Keyword(s):  
Fault Diagnosis ◽  
New Method ◽  
Intelligent Fault Diagnosis ◽  
Multiple Domains

A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation

2021 ◽  
Vol 427 ◽  
pp. 96-109
Author(s):  
Nannan Lu ◽  
Hanhan Xiao ◽  
Yanjing Sun ◽  
Min Han ◽  
Yanfen Wang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
New Method ◽  
Intelligent Fault Diagnosis ◽  
Unsupervised Domain Adaptation

A New Method for Intelligent Fault Diagnosis of Hydroelectric Generating Unit

2007 ◽  
Author(s):  
Zhong Liu ◽  
Jianzhong Zhou ◽  
Min Zou ◽  
Yongchuan Zhang ◽  
Liangliang Zhan
Keyword(s):  
Fault Diagnosis ◽  
New Method ◽  
Intelligent Fault Diagnosis

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

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.

Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation

2021 ◽  
Vol 70 ◽  
pp. 1-9
Author(s):  
Yu Wang ◽  
Xiaojie Sun ◽  
Jie Li ◽  
Ying Yang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis

scholarly journals Research on Intelligent Fault Diagnosis of Servo Hydraulic Drawing Cushion

2021 ◽  
Vol 1966 (1) ◽  
pp. 012031
Author(s):  
Zikou Yu ◽  
Yongyong Duan ◽  
Zongling Wu ◽  
Yuhang Wang
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis
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