An End-to-End Approach for Bearing Fault Diagnosis Based on a Deep Convolution Neural Network

2017 ◽  
pp. 101-109 ◽  
Author(s):  
Liang Chen ◽  
Yuxuan Zhuang ◽  
Jinghua Zhang ◽  
Jianming Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolution Neural Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Deep Convolution Neural Network ◽  
End To End

scholarly journals A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Wei You ◽  
Changqing Shen ◽  
Liang Chen ◽  
Hongbo Que ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Imbalanced Data ◽  
Small Sample ◽  
Convolution Neural Network ◽  
Learning Methods ◽  
Research Attention ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Intelligent bearing fault diagnosis has received much research attention in the field of rotary machinery systems where miscellaneous deep learning methods are generally applied. Among these methods, convolution neural network is particularly powerful because of its ability to learn fruitful features from the original data. However, normal convolutions cannot fully utilize the information along the data flow while the features are being abstracted in deeper layers. To address this problem, a new supervised learning model is proposed for small sample size bearing fault diagnosis with consideration of imbalanced data. This model, which is developed based on a convolution neural network, has a high generalization ability, and its performance is verified by conducting two experiments that use data collected from a self-made bearing test rig. The proposed model demonstrates a favorable performance and is more effective and robust than other deep learning methods.

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