Deep Coupled Visual Perceptual Networks for Motor Fault Diagnosis Under Nonstationary Conditions
Author:
Affiliation:
1. School of Mechanical Engineering, Southeast University, Nanjing, China
2. School of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China
3. School of Engineering, University of British Columbia, Kelowna, BC, Canada
Funder
National Key Research and Development Program of China
Jiangsu Industrial and Information Industry Transformation and Upgrading
National Natural Science Foundation of China
Postgraduate Research & Practice Innovation Program of Jiangsu Province
China Scholarship Council
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/3516/9985401/09763558.pdf?arnumber=9763558
Reference27 articles.
1. Multireceptive Field Denoising Residual Convolutional Networks for Fault Diagnosis
2. Modified Stacked Auto-encoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery
3. Fault Diagnosis of Conventional Circuit Breaker Contact System Based on Time–Frequency Analysis and Improved AlexNet
4. Multiple Wavelet Coefficients Fusion in Deep Residual Networks for Fault Diagnosis
5. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals;wei;SENSORS,2017
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