Meta-Learning for Few-Shot Bearing Fault Diagnosis under Complex Working Conditions

2021 ◽  
Author(s):  
Chuanjiang Li ◽  
Shaobo Li ◽  
Ansi Zhang ◽  
Qiang He
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Meta Learning

Dynamic Joint Distribution Alignment Network for Bearing Fault Diagnosis Under Variable Working Conditions

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Changqing Shen ◽  
Xu Wang ◽  
Dong Wang ◽  
Yongxiang Li ◽  
Jun Zhu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Joint Distribution ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Rolling Bearing Fault Diagnosis Based on Meta-Learning with Few-Shot Samples

2021 ◽  
Author(s):  
Yunpeng He ◽  
Chuanzhi Zang ◽  
Peng Zeng ◽  
Mingxin Wang ◽  
Qingwei Dong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Meta Learning

A new bearing fault diagnosis method using elastic net transfer learning and LSTM

2021 ◽  
pp. 1-9
Author(s):  
Xudong Song ◽  
Dajie Zhu ◽  
Pan Liang ◽  
Lu An
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Short Term Memory ◽  
Elastic Net ◽  
Variable Load ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Model ◽  
Net Transfer

Although the existing transfer learning method based on deep learning can realize bearing fault diagnosis under variable load working conditions, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient£¬which leads to the low accuracy and generalization ability of fault diagnosis model, A fault diagnosis method based on improved elastic net transfer learning under variable load working conditions is proposed. The improved elastic net transfer learning is used to suppress the over fitting and improve the training efficiency of the model, and the long short-term memory network is introduced to train the fault diagnosis model, then a small amount of target domain data is used to fine tune the model parameters. Finally, the fault diagnosis model under variable load working conditions based on improved elastic net transfer learning is constructed. Finally, through model experiments and comparison with conventional deep learning fault diagnosis models such as long short-term memory network (LSTM), gated recurrent unit (GRU) and Bi-LSTM, it shows that the proposed method has higher accuracy and better generalization ability, which verifies the effectiveness of the method.

L2-Norm Shapelet Dictionary Learning-Based Bearing-Fault Diagnosis in Uncertain Working Conditions

2021 ◽  
pp. 1-1
Author(s):  
Jitao Zhang ◽  
Xiaoping Song ◽  
Liang Gao ◽  
Weiming Shen ◽  
Jiahong Chen
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Dictionary Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
L2 Norm

scholarly journals Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 14347-14357 ◽  
Author(s):  
Ran Zhang ◽  
Hongyang Tao ◽  
Lifeng Wu ◽  
Yong Guan
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Bearing Fault Diagnosis Under Variable Working Conditions Based on Domain Adaptation Using Feature Transfer Learning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76187-76197 ◽  
Author(s):  
Zhe Tong ◽  
Wei Li ◽  
Bo Zhang ◽  
Fan Jiang ◽  
Gongbo Zhou
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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