A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data
Joint Authors
Shen, Changqing
You, Wei
Chen, Liang
Que, Hongbo
Huang, Weiguo
Zhu, Zhongkui
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-10
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
You, Wei& Shen, Changqing& Chen, Liang& Que, Hongbo& Huang, Weiguo& Zhu, Zhongkui. 2020. A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data. Shock and Vibration،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1213086
Modern Language Association (MLA)
You, Wei…[et al.]. A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data. Shock and Vibration No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1213086
American Medical Association (AMA)
You, Wei& Shen, Changqing& Chen, Liang& Que, Hongbo& Huang, Weiguo& Zhu, Zhongkui. A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1213086
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213086