Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networks-Reference-Cited by-同舟云学术

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Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networks

Published:2024-05 Issue: Volume:156 Page:111493
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Zou Li^ORCID,Ma Cong,Hu Jun,Yu Zechuan,Zhuang Kejia

Publisher

Elsevier BV

Reference43 articles.

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

2. Magnetic contactless crank-rocker machine;Ishtay;Emerg. Sci. J.,2022

3. Development of deep reinforcement learning-based fault diagnosis method for rotating machinery in nuclear power plants;Qian;Prog. Nucl. Energy,2022

4. An enhanced non-local weakly supervised fault diagnosis method for rotating machinery;Ruan;Measurement,2022

5. An intelligent controller based on LAMDA for speed control of a three-phase inductor motor;Morales;Emerg. Sci. J.,2023

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