Dynamic graph-based feature learning with few edges considering noisy samples for rotating machinery fault diagnosis

2021 ◽  
pp. 1-1
Author(s):  
Kaibo Zhou ◽  
Chaoying Yang ◽  
Jie Liu ◽  
Qi Xu
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Dynamic Graph ◽  
Machinery Fault Diagnosis

Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

2018 ◽  
Vol 305 ◽  
pp. 1-14 ◽  
Author(s):  
Shenghao Tang ◽  
Changqing Shen ◽  
Dong Wang ◽  
Shuang Li ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Machinery Fault Diagnosis

scholarly journals A Rotating Machinery Fault Diagnosis Method Based on Feature Learning of Thermal Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12348-12359 ◽  
Author(s):  
Zhen Jia ◽  
Zhenbao Liu ◽  
Chi-Man Vong ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Thermal Images ◽  
Diagnosis Method ◽  
Machinery 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.

Digital Twin for rotating machinery fault diagnosis in smart manufacturing

2018 ◽  
Vol 57 (12) ◽  
pp. 3920-3934 ◽  
Author(s):  
Jinjiang Wang ◽  
Lunkuan Ye ◽  
Robert X. Gao ◽  
Chen Li ◽  
Laibin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Machinery Fault Diagnosis
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