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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:

Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network
Chunran Huo, Quan Jiang, Yehu Shen, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 105970-105970
Closed Access | Times Cited: 49

Showing 1-25 of 49 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
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
Novel Domain-Adaptive Wasserstein Generative Adversarial Networks for Early Bearing Fault Diagnosis under Various Conditions
Z. K. Hei, Weifang Sun, Haiyang Yang, et al.
Reliability Engineering & System Safety (2025), pp. 110847-110847
Closed Access | Times Cited: 2
Enhanced residual convolutional domain adaptation network with CBAM for RUL prediction of cross-machine rolling bearing
Xingchi Lu, Quan Jiang, Yehu Shen, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 109976-109976
Closed Access | Times Cited: 12
MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
Maoyou Ye, Xiaoan Yan, Xing Hua, et al.
Expert Systems with Applications (2024) Vol. 259, pp. 125214-125214
Closed Access | Times Cited: 11
Electro-hydraulic SBW fault diagnosis method based on novel 1DCNN-LSTM with attention mechanisms and transfer learning
Senhao Zhang, Weihe Liang, Wanzhong Zhao, et al.
Mechanical Systems and Signal Processing (2024) Vol. 220, pp. 111644-111644
Closed Access | Times Cited: 10
A review on convolutional neural network in rolling bearing fault diagnosis
Xin Li, Zengqiang Ma, Zonghao Yuan, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 7, pp. 072002-072002
Closed Access | Times Cited: 9
Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal
Junning Li, Luo Wen-guang, Mengsha Bai
Measurement Science and Technology (2024) Vol. 35, Iss. 9, pp. 092001-092001
Closed Access | Times Cited: 9
Transfer learning rolling bearing fault diagnosis model based on deep feature decomposition and class-level alignment
Jingchuan Dong, Hongyu Jiang, Depeng Su, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 046006-046006
Closed Access | Times Cited: 8
Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network
Xiaoyang Zheng, Peixi Yang, Kai Yan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108087-108087
Closed Access | Times Cited: 7
Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
Ali Saeed, Muazzam A. Khan, Usman Akram, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A new lifelong learning method based on dual distillation for bearing diagnosis with incremental fault types
Shijun Xie, Changqing Shen, Dong Wang, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103136-103136
Closed Access
Self-supervised learning-based dual-classifier domain adaptation model for rolling bearings cross-domain fault diagnosis
Quan Jiang, Xiaoshan Lin, Xingchi Lu, et al.
Knowledge-Based Systems (2023) Vol. 284, pp. 111229-111229
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
Interpretable multi-domain meta-transfer learning for few-shot fault diagnosis of rolling bearing under variable working conditions
Changchang Che, Yuli Zhang, Huawei Wang, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 7, pp. 076103-076103
Closed Access | Times Cited: 4
Denoising diffusion probabilistic model-enabled data augmentation method for intelligent machine fault diagnosis
Pengcheng Zhao, Zhang We, Xiaoshan Cao, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 139, pp. 109520-109520
Closed Access | Times Cited: 4
Source-free Progressive Domain Adaptation Network for Universal Cross-domain Fault Diagnosis of Industrial Equipment
Jipu Li, Ke Yue, Zhaoqian Wu, et al.
IEEE Sensors Journal (2025) Vol. 25, Iss. 5, pp. 8067-8078
Closed Access
Adaptive fault diagnosis of machining processes enabled by hybrid deep learning and incremental transfer learning
Yuchen Liang, Yuqi Wang, Weidong Li, et al.
Computers in Industry (2025) Vol. 167, pp. 104262-104262
Closed Access
Domain Adversarial Transfer Learning Bearing Fault Diagnosis Model Incorporating Structural Adjustment Modules
Zhidan Zhong, Hao Xie, Zhenxin Wang, et al.
Sensors (2025) Vol. 25, Iss. 6, pp. 1851-1851
Open Access
Fault diagnosis of mine main ventilator based on multi-eigenvalue selection and data fusion
Zuzhi Tian, Xiankang Huang, Fangwei Xie, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108211-108211
Closed Access | Times Cited: 3
Adaptive Fusion Transfer Learning-based Digital Multitwin-assised Intelligent Fault Diagnosis
Sizhe Liu, Yongsheng Qi, Liqiang Liu, et al.
Knowledge-Based Systems (2024) Vol. 297, pp. 111923-111923
Closed Access | Times Cited: 3
An Improved Fault Diagnosis Method for Rolling Bearings Based on 1D_CNN Considering Noise and Working Condition Interference
Kai Huang, Linbo Zhu, Zhijun Ren, et al.
Machines (2024) Vol. 12, Iss. 6, pp. 383-383
Open Access | Times Cited: 3
Few-shot learning fault diagnosis of rolling bearings based on siamese network
Xiaoyang Zheng, Zhixia Feng, Zijian Lei, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 9, pp. 095018-095018
Closed Access | Times Cited: 3
Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
Zhenyu Yin, Feiqing Zhang, Guangyuan Xu, et al.
Applied Sciences (2024) Vol. 14, Iss. 3, pp. 1198-1198
Open Access | Times Cited: 3
Intelligent Fault Diagnosis of Unbalanced Samples Using Optimized Generative Adversarial Network
Yan Huo, Diyuan Guan, Dong Ling-yan
Applied Sciences (2024) Vol. 14, Iss. 11, pp. 4927-4927
Open Access | Times Cited: 3
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