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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Gas path fault diagnosis of aircraft engine using HELM and transfer learning
Junqiang Liu
Engineering Applications of Artificial Intelligence (2022) Vol. 114, pp. 105149-105149
Closed Access | Times Cited: 18

Showing 18 citing articles:

Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery
Shengnan Tang, Jingtao Ma, Zhengqi Yan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 134, pp. 108678-108678
Closed Access | Times Cited: 30
An uncertainty perception metric network for machinery fault diagnosis under limited noisy source domain and scarce noisy unknown domain
Changdong Wang, Jingli Yang, Huamin Jie, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102682-102682
Closed Access | Times Cited: 30
Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions
Serhii Vladov, Łukasz Ścisło, Валерій Сокуренко, et al.
Sensors (2024) Vol. 24, Iss. 13, pp. 4246-4246
Open Access | Times Cited: 18
Weighted class-aware matching adaptation network for aero-engine imbalanced multi-source cross-domain fault diagnosis under class shift
Yuqiang Wang, Yong-Ping Zhao, Bo Liang, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 149, pp. 110510-110510
Closed Access
Entropy-based domain adaption strategy for predicting remaining useful life of rolling element bearing
Anil Kumar, Chander Parkash, Yuqing Zhou, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108575-108575
Closed Access | Times Cited: 4
Continual contrastive reinforcement learning: Towards stronger agent for environment-aware fault diagnosis of aero-engines through long-term optimization under highly imbalance scenarios
Haoze Wu, Shisheng Zhong, Minghang Zhao, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103297-103297
Closed Access
Failure Analysis and Intelligent Identification of Critical Friction Pairs of an Axial Piston Pump
Yong Zhu, Tao Zhou, Shengnan Tang, et al.
Journal of Marine Science and Engineering (2023) Vol. 11, Iss. 3, pp. 616-616
Open Access | Times Cited: 7
Fault diagnosis of monoblock centrifugal pumps using pre-trained deep learning models and scalogram images
C. V. Prasshanth, Naveen Venkatesh Sridharan, Tapan K. Mahanta, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 109022-109022
Closed Access | Times Cited: 2
Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System
Serhii Vladov, Maryna Bulakh, Victoria Vysotska, et al.
Energies (2024) Vol. 17, Iss. 16, pp. 4195-4195
Open Access | Times Cited: 2
Robust key parameter identification of dedicated hybrid engine performance indicators via K-fold filter collaborated feature selection
Xu He, Ji Li, Quan Zhou, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107114-107114
Open Access | Times Cited: 6
Helicopters Turboshaft Engines Parameters Identification at Flight Modes Using Neural Networks
Serhii Vladov, Marina Petchenko, Yurii Shmelov, et al.
2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT) (2022)
Closed Access | Times Cited: 8
A Dual-Dimension Convolutional-Attention Module for Remaining Useful Life Prediction of Aeroengines
Yixin Zhu, Zhidan Liu
Aerospace (2024) Vol. 11, Iss. 10, pp. 809-809
Open Access | Times Cited: 1
Deep hierarchical sorting networks for fault diagnosis of aero-engines
Jin‐Lei Wu, Lin Lin, Dan Liu, et al.
Computers in Industry (2024) Vol. 165, pp. 104229-104229
Closed Access | Times Cited: 1
The Lean Blowout Prediction Techniques in Lean Premixed Gas Turbine: An Overview
Abdulrahman Abdullah Bahashwan, Rosdiazli Ibrahim, Madiah Omar, et al.
Energies (2022) Vol. 15, Iss. 22, pp. 8343-8343
Open Access | Times Cited: 7
Multi-Head Attention-Based Hybrid Deep Neural Network for Aeroengine Risk Assessment
Jian-Hang Li, Xinyue Gao, Xiang Lu, et al.
IEEE Access (2023) Vol. 11, pp. 113376-113389
Open Access | Times Cited: 3
Supervised learning-based multi-site lean blowout prediction for dry low emission gas turbine
Abdulrahman Abdullah Bahashwan, Rosdiazli Ibrahim, Madiah Omar, et al.
Expert Systems with Applications (2023) Vol. 244, pp. 123035-123035
Closed Access | Times Cited: 2
An Equipment Anomaly Diagnosis Method Based on Deep Learning
Mingshu Ren, Qiong Jiang, Chengyi Zhou, et al.
Journal of Circuits Systems and Computers (2024)
Closed Access
Structural Damage Diagnosis of Aerospace CFRP Components: Leveraging Transfer Learning in the Matching Networks Framework
Zhuojun Xu, Hao Li, Jianbo Yu
Structural Control and Health Monitoring (2024) Vol. 2024, Iss. 1
Open Access
Domain Adversarial Enhanced Multi-Channel Graph Networks for Aeroengine Gas Path Fault Diagnosis
Changyi Xu, Xuecheng Gui, Mingxiao Huo
IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society (2023), pp. 1-6
Closed Access
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