A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

2017 ◽  
Vol 95 ◽  
pp. 187-204 ◽  
Author(s):  
Haidong Shao ◽  
Hongkai Jiang ◽  
Huiwei Zhao ◽  
Fuan Wang
Keyword(s):  
Fault Diagnosis ◽  
Feature Learning ◽  
Rotating Machinery ◽  
Learning Method ◽  
Machinery Fault Diagnosis

scholarly journals Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation

2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110402
Author(s):  
Jiajie Shao ◽  
Zhiwen Huang ◽  
Yidan Zhu ◽  
Jianmin Zhu ◽  
Dianjun Fang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Diagnostic Performance ◽  
Domain Adaptation ◽  
Rotating Machinery ◽  
Learning Method ◽  
Migration Ability ◽  
Adversarial Learning ◽  
Machinery Fault Diagnosis

Rotating machinery fault diagnosis is very important for industrial production. Many intelligent fault diagnosis technologies are successfully applied and achieved good results. Due to the fact that machine damages usually happen under different working conditions, and manual scale labeled data are too expensive, domain adaptation has been developed for fault diagnosis. However, the current methods mostly focus on global domain adaptation, the application of subdomain adaptation for fault diagnosis is still limited. A deep transfer learning method is proposed for rotating machinery fault diagnosis in this study, where subdomain adaptation and adversarial learning are introduced to align local feature distribution and global feature distribution separately. Experiments are performed on two rotating machinery datasets to verify the effectiveness of this method. The results reveal that this method has outstanding mutual migration ability and can improve the diagnostic performance.

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.

Sign in / Sign up

Export Citation Format

Share Document