Fault Diagnosis of Variable Working Conditions Based on Transfer Learning and Multi-channel CNN-LSTM Network

Author:

Che Kang1,Jin Yongze1,Mu Lingxia1,Li Yankai1,Zhang Jian1,Xie Guo1

Affiliation:

1. Xi'an University of Technology,School of Automation and Information Engineering,Xi’an,China

Funder

National Science Foundation

Natural Science Foundation of Shaanxi Province

Publisher

IEEE

Reference15 articles.

1. Cross-domain intelligent bearing fault diagnosis under class imbalanced samples via transfer residual network augmented with explicit weight self-assignment strategy based on meta data

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4. Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder;li;IEEE Access,2017

5. Domain-adversarial training of neural networks;ganin;The Journal of Machine Learning Research,2016

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