A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-Case Machines

2018 ◽  
Author(s):  
Bin Yang ◽  
Yaguo Lei ◽  
Feng Jia ◽  
Saibo Xing
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Real Case ◽  
Learning Method ◽  
Intelligent Fault Diagnosis

Generative Adversarial Network and Transfer Learning Based Fault Detection for Rotating Machinery with Imbalance Data Condition

2021 ◽  
Author(s):  
jun li ◽  
Yongbao Liu ◽  
Qijie Li
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Learning Method ◽  
Intelligent Fault Diagnosis ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Proposed Model ◽  
Diagnosis Method

Abstract Intelligent fault diagnosis achieves tremendous success in machine fault diagnosis because of the outstanding data-driven capability. However, the severe imbalanced dataset in practical scenarios of industrial rotating machinery is still a big challenge for the development of intelligent fault diagnosis method. In this paper, we solve this issue by constructing a novel deep learning model incorporated with a transfer learning method based on the Time-GAN and Efficient-Net models. Firstly, the proposed model so called Time-GAN-TL extends the imbalanced fault diagnosis of rolling bearings by using time series generative adversarial network. Secondly, balanced vibration signals are converted into two-dimensional images for training and classification by implementing the Efficient-Net into the transfer learning method. Finally, the proposed method is validated using two-types of rolling bearing experimental data. The high-precision diagnosis results of the transfer learning experiments and the comparison with other representative fault diagnosis classification methods reveal the efficiency, reliability, and generalization performance of the presented model.

scholarly journals Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jun He ◽  
Xiang Li ◽  
Yong Chen ◽  
Danfeng Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Learning Method ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

An adaptive deep transfer learning method for bearing fault diagnosis

Measurement ◽  
2020 ◽  
Vol 151 ◽  
pp. 107227 ◽  
Author(s):  
Zhenghong Wu ◽  
Hongkai Jiang ◽  
Ke Zhao ◽  
Xingqiu Li
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Learning Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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