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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:

A hybrid of FEM simulations and generative adversarial networks to classify faults in rotor-bearing systems
Yun Gao, Xiaoyang Liu, Haizhou Huang, et al.
ISA Transactions (2020) Vol. 108, pp. 356-366
Closed Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Weihua Li, Ruyi Huang, Jipu Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 167, pp. 108487-108487
Open Access | Times Cited: 527
A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
Mohammed Hakim, Abdoulhdi A. Borhana Omran, Ali Najah Ahmed, et al.
Ain Shams Engineering Journal (2022) Vol. 14, Iss. 4, pp. 101945-101945
Open Access | Times Cited: 135
Adaptive variational autoencoding generative adversarial networks for rolling bearing fault diagnosis
Xin Wang, Hongkai Jiang, Zhenghong Wu, et al.
Advanced Engineering Informatics (2023) Vol. 56, pp. 102027-102027
Closed Access | Times Cited: 100
Machinery Fault Diagnosis Based on Domain Adaptation to Bridge the Gap Between Simulation and Measured Signals
Yunxia Lou, Anil Kumar, Jiawei Xiang
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-9
Closed Access | Times Cited: 87
A bearing fault diagnosis method without fault data in new working condition combined dynamic model with deep learning
Kun Xu, Xianguang Kong, Qibin Wang, et al.
Advanced Engineering Informatics (2022) Vol. 54, pp. 101795-101795
Closed Access | Times Cited: 70
Online bearing fault diagnosis using numerical simulation models and machine learning classifications
Hui Wang, Junkang Zheng, Jiawei Xiang
Reliability Engineering & System Safety (2023) Vol. 234, pp. 109142-109142
Closed Access | Times Cited: 68
Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review
Shaohua Qiu, Xiaopeng Cui, Zuowei Ping, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1305-1305
Open Access | Times Cited: 67
Fault diagnosis in rotating machines based on transfer learning: Literature review
Iqbal Misbah, C.K.M. Lee, K. L. Keung
Knowledge-Based Systems (2023) Vol. 283, pp. 111158-111158
Closed Access | Times Cited: 61
Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks
Cuixia Jiang, Hao Chen, Qifa Xu, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 4, pp. 1667-1681
Closed Access | Times Cited: 51
Bearing fault detection with vibration and acoustic signals: Comparison among different machine leaning classification methods
Josué Pacheco-Chérrez, Jesús Anselmo Fortoul-Diaz, Froylán Cortés-Santacruz, et al.
Engineering Failure Analysis (2022) Vol. 139, pp. 106515-106515
Closed Access | Times Cited: 50
Fault diagnosis of mechanical equipment in high energy consumption industries in China: A review
Yongjian Sun, Jian Wang, Xiaohong Wang
Mechanical Systems and Signal Processing (2022) Vol. 186, pp. 109833-109833
Closed Access | Times Cited: 46
Intelligent fault diagnosis of bearings under small samples: A mechanism-data fusion approach
Kun Xu, Xianguang Kong, Qibin Wang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107063-107063
Closed Access | Times Cited: 30
Simulation-Driven Subdomain Adaptation Network for bearing fault diagnosis with missing samples
Jianing Liu, Hongrui Cao, Shuaiming Su, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106201-106201
Closed Access | Times Cited: 29
Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery
Jianchun Guo, Zetian Si, Jiawei Xiang
ISA Transactions (2023) Vol. 138, pp. 546-561
Closed Access | Times Cited: 22
Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction
Ismail Nejjar, Fabian Geissmann, Mengjie Zhao, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109718-109718
Open Access | Times Cited: 22
Incremental learning with multi-fidelity information fusion for digital twin-driven bearing fault diagnosis
Xufeng Huang, Tingli Xie, Shuyang Luo, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108212-108212
Closed Access | Times Cited: 8
Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning
Sihan Wang, Dazhi Wang, Deshan Kong, et al.
Sensors (2020) Vol. 20, Iss. 22, pp. 6437-6437
Open Access | Times Cited: 65
A novel method for rotor fault diagnosis based on deep transfer learning with simulated samples
Ling Xiang, Xingyu Zhang, Zhang Yue, et al.
Measurement (2022) Vol. 207, pp. 112350-112350
Closed Access | Times Cited: 35
Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults
Jianchun Guo, Zetian Si, Yi Liu, et al.
Reliability Engineering & System Safety (2022) Vol. 224, pp. 108533-108533
Closed Access | Times Cited: 29
Dynamic model-driven intelligent fault diagnosis method for rotary vector reducers
Junkang Zheng, Hui Wang, Anil Kumar, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106648-106648
Closed Access | Times Cited: 21
Latest innovations in the field of condition-based maintenance of rotatory machinery: a review
Anil Kumar, Chander Parkash, Hesheng Tang, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 2, pp. 022003-022003
Closed Access | Times Cited: 20
Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain
Mohammed Hakim, Abdoulhdi A. Borhana Omran, Jawaid I. Inayat-Hussain, et al.
Sensors (2022) Vol. 22, Iss. 15, pp. 5793-5793
Open Access | Times Cited: 26
Numerical simulation of gears for fault detection using artificial intelligence models
Hui Wang, Ronggang Yang, Jiawei Xiang
Measurement (2022) Vol. 203, pp. 111898-111898
Closed Access | Times Cited: 26
Fault diagnosis of planetary gears based on intrinsic feature extraction and deep transfer learning
Huan Li, Yong Lv, Rui Yuan, et al.
Measurement Science and Technology (2022) Vol. 34, Iss. 1, pp. 014009-014009
Closed Access | Times Cited: 26
Signal-to-Image: Rolling Bearing Fault Diagnosis Using ResNet Family Deep-Learning Models
Guoguo Wu, Xuerong Ji, Guolai Yang, et al.
Processes (2023) Vol. 11, Iss. 5, pp. 1527-1527
Open Access | Times Cited: 15
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