Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning
Author:
Affiliation:
1. School of Mechanical Engineer, Yancheng Institute of Technology, Yancheng 224051, China
2. College of Computer and Information Engineering, Hohai University, Nanjing 211100, China
Abstract
Funder
Natural Science Research of Jiangsu Higher Education Institutions of China
Publisher
Hindawi Limited
Subject
Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering
Link
http://downloads.hindawi.com/journals/sv/2020/4676701.pdf
Reference20 articles.
1. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
2. Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine
3. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults
4. Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network
5. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
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