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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 new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem
Yunjia Dong, Yuqing Li, Huailiang Zheng, et al.
ISA Transactions (2021) Vol. 121, pp. 327-348
Closed Access | Times Cited: 149

Showing 26-50 of 149 citing articles:

Self-learning transferable neural network for intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data
Zenghui An, Xingxing Jiang, Jing Cao, et al.
Knowledge-Based Systems (2021) Vol. 230, pp. 107374-107374
Closed Access | Times Cited: 52
Efficient federated convolutional neural network with information fusion for rolling bearing fault diagnosis
Zehui Zhang, Xiaobin Xu, Wenfeng Gong, et al.
Control Engineering Practice (2021) Vol. 116, pp. 104913-104913
Closed Access | Times Cited: 49
Transfer learning with limited labeled data for fault diagnosis in nuclear power plants
Jiangkuan Li, Meng Lin, Yankai Li, et al.
Nuclear Engineering and Design (2022) Vol. 390, pp. 111690-111690
Closed Access | Times Cited: 35
Experimental study on performance assessments of HVAC cross-domain fault diagnosis methods oriented to incomplete data problems
Qiang Zhang, Zhe Tian, Yakai Lu, et al.
Building and Environment (2023) Vol. 236, pp. 110264-110264
Closed Access | Times Cited: 19
Research on Digital Twin Driven Rolling Bearing Model-Data Fusion Life Prediction Method
Wentao Zhao, Chao Zhang, Jianguo Wang, et al.
IEEE Access (2023) Vol. 11, pp. 48611-48627
Open Access | Times Cited: 16
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: 6
A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation
Jianqun Zhang, Qing Zhang, Xianrong Qin, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 6, pp. 066107-066107
Closed Access | Times Cited: 5
Hypergraph construction using Multi-Sensor for helicopter Tail-Drive system fault diagnosis
Aijun Yin, Zhaoyi Sun, Junlin Zhou
Measurement (2024) Vol. 231, pp. 114586-114586
Closed Access | Times Cited: 5
Prior knowledge-informed multi-task dynamic learning for few-shot machinery fault diagnosis
Tianci Zhang, Jinglong Chen, Zhi‐Sheng Ye, et al.
Expert Systems with Applications (2025), pp. 126439-126439
Closed Access
Unlocking the power of knowledge for few-shot fault diagnosis: A review from a knowledge perspective
Pei Ling Lai, Fan Zhang, Tianrui Li, et al.
Information Sciences (2025), pp. 121996-121996
Closed Access
Digital twin-driven operational CycleGAN-based multiple virtual-physical mappings for remaining useful life prediction under limited life cycle data
Quanning Xu, Zihao Lei, Gu Shu-Long, et al.
Journal of Advanced Research (2025)
Open Access
Envelope Spectrum Knowledge-Guided Domain Invariant Representation Learning Strategy for Intelligent Fault Diagnosis of Bearing
Zhi Tang, Lin Bo, Hao Bai, et al.
ISA Transactions (2025)
Closed Access
Dynamic model-assisted disentanglement framework for rolling bearing fault diagnosis under time-varying speed conditions
Yuhui Xu, Yimin Jiang, Tangbin Xia, et al.
Mechanical Systems and Signal Processing (2025) Vol. 230, pp. 112588-112588
Closed Access
Digital twin model for chiller fault diagnosis based on SSAE and transfer learning
Xin Ma, Fan Chen, Zhihan Wang, et al.
Building and Environment (2023) Vol. 243, pp. 110718-110718
Closed Access | Times Cited: 14
VIT-GADG: A Generative Domain-Generalized Framework for Chillers Fault Diagnosis Under Unseen Working Conditions
Kexin Jiang, Xuejin Gao, Huihui Gao, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-13
Closed Access | Times Cited: 12
Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning
Jingjing Liu, Jie-Peng Yao, Jin-Hang Liu, et al.
Applied Intelligence (2024) Vol. 54, Iss. 3, pp. 2528-2550
Closed Access | Times Cited: 4
Multi-source domain self-supervised enhanced transfer fault diagnosis approach with source sample refinement strategy
Xinyu Ren, Wanli Zhao, Mengmeng Liu, et al.
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110380-110380
Closed Access | Times Cited: 4
A physics-based sample generation method for few-shot bearing condition monitoring
Zepeng Ma, Lei Fu, Fang Xu, et al.
Knowledge-Based Systems (2025), pp. 112952-112952
Closed Access
A mechanism-based sample generation method utilizing morphological analysis and dynamic modeling for online monitoring of rotor systems
Zepeng Ma, Lei Fu, Dapeng Tan, et al.
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112438-112438
Closed Access
An efficient transfer fault diagnosis method integrating feature redundancy selection and multi-strategy parameter optimization
Wenchao Jia, Aijun An, Bin Gong, et al.
Expert Systems with Applications (2025), pp. 127267-127267
Closed Access
Multilevel feature encoder for transfer learning-based fault detection on acoustic signal
Dezheng Wang, Congyan Chen
Information Fusion (2025), pp. 103128-103128
Closed Access
Causal Disentanglement: A Generalized Bearing Fault Diagnostic Framework in Continuous Degradation Mode
Jie Li, Yu Wang, Yanyang Zi, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 34, Iss. 9, pp. 6250-6262
Closed Access | Times Cited: 31
A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data
Qiang Zhang, Zhe Tian, Jide Niu, et al.
Building and Environment (2022) Vol. 225, pp. 109641-109641
Closed Access | Times Cited: 20
Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion
Di Yu, H.C. Fu, Yanchen Song, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 015013-015013
Closed Access | Times Cited: 12
Cloud ensemble learning for fault diagnosis of rolling bearings with stochastic configuration networks
Wei Dai, Jiang Liu, Lanhao Wang
Information Sciences (2023) Vol. 658, pp. 119991-119991
Open Access | Times Cited: 11

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