SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samples

Author:

Yao Xuejian,Lu Xingchi,Jiang QuanshengORCID,Shen Yehu,Xu Fengyu,Zhu Qixin

Publisher

Elsevier BV

Reference48 articles.

1. An open set diagnosis method for rolling bearing faults based on prototype and reconstructed integrated network;Sun;IEEE Trans. Instrum. Meas.,2023

2. Imbalanced sample fault diagnosis of rolling bearing using deep condition multidomain generative adversarial network;Liu;IEEE Sens. J.,2023

3. A novel knowledge sharing method for rolling bearing fault detection against impact of different signal sampling frequencies;Chen;IEEE Trans. Instrum. Meas.,2023

4. Attribute fusion transfer for zero-shot fault diagnosis;Fan;Adv. Eng. Inf.,2023

5. Category-aware dual adversarial domain adaptation model for rolling bearings fault diagnosis under variable conditions;Lu;Meas. Sci. Technol.,2023

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