Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning
Joint Authors
Zhou, Jianye
Shao, Siyu
Bian, Gangying
Zhang, Lin
Yang, Xinyu
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-08
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed.
Compared with existing methods, this method has faster training speed, fewer training samples per time, and higher accuracy.
First, the raw data collected by multiple sensors are combined into a graph and normalized to facilitate model training.
Next, the transposed convolution is utilized to expand the image resolution, and then the images are treated as the input of the transfer learning model for training and fine-tuning.
The proposed method adopts 512 time series to conduct experiments on two main mechanical datasets of bearings and gears in the variable-speed gearbox, which verifies the effectiveness and versatility of the method.
We have obtained advanced results on both datasets of the gearbox dataset.
The dataset shows that the test accuracy is 99.99%, achieving a significant improvement from 98.07% to 99.99%.
American Psychological Association (APA)
Zhou, Jianye& Yang, Xinyu& Zhang, Lin& Shao, Siyu& Bian, Gangying. 2020. Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning. Shock and Vibration،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213010
Modern Language Association (MLA)
Zhou, Jianye…[et al.]. Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning. Shock and Vibration No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1213010
American Medical Association (AMA)
Zhou, Jianye& Yang, Xinyu& Zhang, Lin& Shao, Siyu& Bian, Gangying. Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213010
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213010