A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples-Reference-Cited by-同舟云学术

A Novel Deep Offline-to-Online Transfer Learning Framework for Pipeline Leakage Detection With Small Samples

Published:2023 Issue: Volume:72 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Wang Chuang¹^ORCID,Wang Zidong¹^ORCID,Liu Weibo¹^ORCID,Shen Yuxuan²^ORCID,Dong Hongli³^ORCID

Affiliation:

1. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, U.K

2. Artificial Intelligence Energy Research Institute and the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, China

3. Sanya Offshore Oil and Gas Research Institute, Northeast Petroleum University, Sanya, China

Funder

National Natural Science Foundation of China

Hainan Province Science and Technology Special Fund of China

Natural Science Foundation of Heilongjiang Province of China

Alexander von Humboldt Foundation of Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/09959883.pdf?arnumber=9959883

Reference61 articles.

1. Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

2. Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

3. Domain Adaptation via Transfer Component Analysis

4. Leak Localization of Gas Pipeline Based on the Combination of EEMD and Cross-spectrum Analysis

5. Deep Model Based Domain Adaptation for Fault Diagnosis

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