Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer
Joint Authors
Yang, Jingli
Gao, Tianyu
Jiang, Shouda
Li, Shijie
Tang, Qing
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-09
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
In actual engineering applications, inevitable noise seriously affects the accuracy of fault diagnosis for rotating machinery.
To effectively identify the fault classes of rotating machinery under noise interference, an efficient fault diagnosis method without additional denoising procedures is proposed.
First, a one-dimensional deep residual shrinkage network, which directly takes the raw vibration signals contaminated by noise as input, is developed to realize end-to-end fault diagnosis.
Then, to further enhance the noise immunity of the diagnosis model, the first layer of the model is set to a wide convolution layer to extract short time features.
Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise.
Experimental results illustrate that the fault diagnosis model for rotating machinery based on one-dimensional deep residual shrinkage network with a wide convolution layer (1D-WDRSN) can accurately identify the fault classes even under noise interference.
American Psychological Association (APA)
Yang, Jingli& Gao, Tianyu& Jiang, Shouda& Li, Shijie& Tang, Qing. 2020. Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer. Shock and Vibration،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213136
Modern Language Association (MLA)
Yang, Jingli…[et al.]. Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer. Shock and Vibration No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1213136
American Medical Association (AMA)
Yang, Jingli& Gao, Tianyu& Jiang, Shouda& Li, Shijie& Tang, Qing. Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213136
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213136