Deep Residual Shrinkage Network With Time-Frequency Features For Bearing Fault Diagnosis
Author:
Affiliation:
1. Jiangsu University,School of Computer Science and Communication Engineering,Zhenjiang,China,212013
2. Focal Acoustics AI Research Centre,Xi’an,China,710076
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9984156/9984215/09984603.pdf?arnumber=9984603
Reference26 articles.
1. Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis
2. Intelligent Fault Diagnosis of Bearing Based on Stacked Denoising Autoencoders
3. Bearing Intelligent Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network
4. Fault Diagnosis of Bearing Based on Variational Mode Decomposition and Deep Learning
5. An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. FEW-SHOT BEARING FAULT DIAGNOSIS IN TIME-FREQUENCY MAPS USING PROTOTYPICAL NETWORKS;2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC);2023-11-14
2. Efficient Conflict-Filtered Network for Defect Detection;IEEE Transactions on Instrumentation and Measurement;2023
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