A Fault Diagnosis Method of Aircraft Hydraulic System Based on SSA-DBN-Reference-Cited by-同舟云学术

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A Fault Diagnosis Method of Aircraft Hydraulic System Based on SSA-DBN

Published:2022-08-15 Issue: Volume: Page:
ISSN:
Container-title:2022 34th Chinese Control and Decision Conference (CCDC)
language:
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Author:

Cui Jianguo¹,Song Xue¹,Cui Xiao²,Du Wenyou¹,Liu Dong¹,Yu Mingyue¹,Jiang Liying¹,Wang Jinglin³

Affiliation:

1. Shenyang Aerospace University,School of Automation,Shenyang,China,110136

2. AVIC Aerodynamics Research Institute,Model Balance and Wind Tunnel Equipment Department 5,Shenyang,China,110034

3. Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health Management,Shanghai,China,201601

Funder

Nature

Aeronautical Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10032843/10032844/10034292.pdf?arnumber=10034292

Reference14 articles.

1. Power transformer fault classifying model based on deep belief network[J];shi;Power system protection and control,2016

2. Application of Deep Learning Fusion Model in Mechanical Fault Diagnosis[J];wang;Vibration Test and Diagnosis,2019

3. The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions[J];a;IEEE Transactions on Pattern Analysis and Machine Intelligence,2014

4. Fault diagnosis method of traction motor bearing based on deep belief network[J];tu;Urban Rail Transit Research,2020

5. Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning

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