A deep reinforcement transfer convolutional neural network for rolling bearing fault diagnosis-Reference-Cited by-同舟云学术

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A deep reinforcement transfer convolutional neural network for rolling bearing fault diagnosis

Published:2022-10 Issue: Volume:129 Page:505-524
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Wu Zhenghong,Jiang Hongkai,Liu Shaowei,Wang Ruixin

Funder

National Natural Science Foundation of China

Aeronautical Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference53 articles.

1. Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO;Shao;ISA Trans,2020

2. Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions;He;Knowl-Based Syst,2020

3. Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis;Liu;Measurement,2021

4. Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals;Wang;ISA Trans,2021

5. Multiscale local features learning based on p neural network for rolling bearing intelligent fault diagnosis;Li;Measurement,2020

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3. Resilient fast nonlinear blind deconvolution with uniform initialization for the bearing fault diagnosis;Structural Health Monitoring;2024-08-08

4. SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samples;Advanced Engineering Informatics;2024-08

5. Adaptive fast nonlinear blind deconvolution based on nonuniform particle swarm optimization for the rolling bearing fault diagnosis;Measurement;2024-08

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