Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning

Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning

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....

Full description

Bibliographic Details
Main Authors:	Jianye Zhou, Xinyu Yang, Lin Zhang, Siyu Shao, Gangying Bian
Format:	Article
Language:	English
Published:	Hindawi Limited 2020-01-01
Series:	Shock and Vibration
Online Access:	http://dx.doi.org/10.1155/2020/8863388

Similar Items

A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery
by: Shangjun Ma, et al.
Published: (2019-05-01)

Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation
by: Jiajie Shao, et al.
Published: (2021-08-01)

Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions
by: Shangjun Ma, et al.
Published: (2019-01-01)

Application of Deep Learning in Fault Diagnosis of Rotating Machinery
by: Wanlu Jiang, et al.
Published: (2021-05-01)

Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network
by: Yibing Li, et al.
Published: (2019-01-01)

Fault diagnosis of rotating machinery
by: Edwards, S.
Published: (1999)

A multisignal detection of hazardous materials for homeland security
by: Alamaniotis Miltiadis, et al.
Published: (2009-01-01)

Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer
by: Jingli Yang, et al.
Published: (2020-01-01)

Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks
by: Hongmei Li, et al.
Published: (2020-07-01)

Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition
by: Dong Liu, et al.
Published: (2020-01-01)

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition
by: Yuan Xie, et al.
Published: (2017-01-01)

Research Progress of Rotating Machinery Fault Diagnosis Based on Deep Learning
by: Du Zhehua, et al.
Published: (2021-01-01)

Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network
by: Baoru Han, et al.
Published: (2021-01-01)

Spiking VGG7: Deep Convolutional Spiking Neural Network with Direct Training for Object Recognition
by: Jiang, S., et al.
Published: (2022)

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG
by: Zia Ullah, et al.
Published: (2020-07-01)

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network
by: Sheng Guo, et al.
Published: (2018-05-01)

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery
by: Shengnan Tang, et al.
Published: (2020-01-01)

Optimization design of binary VGG convolutional neural network accelerator
by: Zhang Xuxin, et al.
Published: (2021-02-01)

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery
by: Xiaojie Guo, et al.
Published: (2016-12-01)

Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection
by: Dongying Han, et al.
Published: (2020-12-01)

Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery
by: Shengnan Tang, et al.
Published: (2020-01-01)

Software design of rotating machinery fault diagnosis system based on deep learning
by: He Xiaofeng, et al.
Published: (2021-01-01)

Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery
by: Yumei Qi, et al.
Published: (2017-01-01)

Investigations in Fault Diagnosis Methods for Rotating Machinery
by: Chun-Chieh Wang, et al.

Fault Diagnosis of Vibrational Signature For Rotating Machinery
by: Ching Su Yang, et al.
Published: (1999)

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images
by: Mohammad Shahjahan Majib, et al.
Published: (2021-01-01)

Lung segmentation method with dilated convolution based on VGG-16 network
by: Lei Geng, et al.
Published: (2019-10-01)

An Approach Focusing on the Convolutional Layer Characteristics of the VGG Network for Vehicle Tracking
by: Danlu Zhang, et al.
Published: (2020-01-01)

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
by: Chuan Li, et al.
Published: (2016-06-01)

Impact of Approximate Multipliers on VGG Deep Learning Network
by: Issam Hammad, et al.
Published: (2018-01-01)

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery
by: Sixiang Jia, et al.
Published: (2021-04-01)

A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks
by: Jingli Yang, et al.
Published: (2020-07-01)

Research on Rotating Machinery Fault Diagnosis Method Based on Energy Spectrum Matrix and Adaptive Convolutional Neural Network
by: Yiyang Liu, et al.
Published: (2021-12-01)

An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
by: Huihui Qiao, et al.
Published: (2019-01-01)

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery
by: Shengnan Tang, et al.
Published: (2020-09-01)

Identification Procedures as Tools for Fault Diagnosis of Rotating Machinery
by: Susanne Seibold, et al.
Published: (1995-01-01)

A New Fault Diagnosis Method of Rotating Machinery
by: Chih-Hao Chen, et al.
Published: (2008-01-01)

Application of Adaptive Algorithm in Rotating Machinery Fault Diagnosis
by: Chin-Wei Huang, et al.
Published: (2004)

Fault Diagnosis System Development for Rotating Electrical Machinery
by: Jen-Chen Tsai, et al.
Published: (2011)

Using Convolution Neural Network for Vibration Fault Diagnosis in Machinery
by: Yeh, Chiao-Wei, et al.
Published: (2018)