An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples

Author:

Song Baoye,Liu Yiyan,Fang Jingzhong,Liu WeiboORCID,Zhong Maiying,Liu Xiaohui

Publisher

Elsevier BV

Reference51 articles.

1. An interpretable anti-noise network for rolling bearing fault diagnosis based on FSWT;Sun;Measurement,2022

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3. Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey;Zhang;Chin. J. Aeronaut.,2023

4. Deep transfer learning for bearing fault diagnosis: A systematic review since 2016;Chen;IEEE Trans. Instrum. Meas.,2023

5. Deep learning-based intelligent fault diagnosis methods toward rotating machinery;Tang;IEEE Access,2020

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