Deep feature interactive network for machinery fault diagnosis using multi-source heterogeneous data-Reference-Cited by-同舟云学术

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Deep feature interactive network for machinery fault diagnosis using multi-source heterogeneous data

Published:2024-02 Issue: Volume:242 Page:109795
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Miao Mengqi^ORCID,Yu Jianbo

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Science and Technology Innovation Plan Of Shanghai Science and Technology Commission

National Natural Science Foundation of China National Outstanding Youth Science Fund Project

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference35 articles.

1. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing;Zhang;Reliab Eng Syst Saf,2023

2. Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine;Liu;Reliab Eng Syst Saf,2023

3. Sparse-representation-network-based feature learning of vibration signal for machinery fault diagnosis;Miao;IEEE Trans Ind Inf,2023

4. Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging;Li;Reliab Eng Syst Saf,2023

5. A multisensor information fusion method for high-reliability fault diagnosis of rotating machinery;Huo;IEEE Trans Instrum Meas,2022

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Reserving embedding space for new fault types: A new continual learning method for bearing fault diagnosis;Reliability Engineering & System Safety;2024-12

2. Temporal self-supervised domain adaptation network for machinery fault diagnosis under multiple non-ideal conditions;Reliability Engineering & System Safety;2024-11

3. Causal intervention graph neural network for fault diagnosis of complex industrial processes;Reliability Engineering & System Safety;2024-11

4. Single domain generalization method based on anti-causal learning for rotating machinery fault diagnosis;Reliability Engineering & System Safety;2024-10

5. Multi-Source Feature-Fusion Method for the Seismic Data of Cultural Relics Based on Deep Learning;Sensors;2024-07-12

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