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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 feature boosting based continual learning method for bearing fault diagnosis with incremental fault types
Zhenzhong He, Changqing Shen, Bojian Chen, et al.
Advanced Engineering Informatics (2024) Vol. 61, pp. 102469-102469
Closed Access | Times Cited: 11

Showing 11 citing articles:

A new cross-domain bearing fault diagnosis method with few samples under different working conditions
Xingjun Dong, Changsheng Zhang, Hanrui Liu, et al.
Journal of Manufacturing Processes (2025) Vol. 135, pp. 359-374
Closed Access
Dynamic branch layer fusion: A new continual learning method for rotating machinery fault diagnosis
Changqing Shen, Zhenzhong He, Bojian Chen, et al.
Knowledge-Based Systems (2025), pp. 113177-113177
Closed Access
Adaptive maximum generalized Gaussian cyclostationarity blind deconvolution for the early fault diagnosis of high-speed train bearings under non-Gaussian noise
Defu Han, Hongyuan Qi, S. K. Wang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102731-102731
Closed Access | Times Cited: 4
A novel incremental method for bearing fault diagnosis that continuously incorporates unknown fault types
Haoxiang He, Cunbo Zhuang, Hui Xiong
Mechanical Systems and Signal Processing (2024) Vol. 216, pp. 111524-111524
Closed Access | Times Cited: 3
Adaptive feature consolidation residual network for exemplar-free continuous diagnosis of rotating machinery with fault-type increments
Yan Zhang, Changqing Shen, Xingli Zhong, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102715-102715
Closed Access | Times Cited: 2
Enhancing induction machine fault detection through machine learning: Time and frequency analysis of vibration signals
A. Daas, Bilal Sari, Jiajia Jia, et al.
Measurement (2024), pp. 116023-116023
Closed Access | Times Cited: 1
SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM)
Zhiwei Xu, Tao Liu, Zezhou Xia, et al.
Sensors (2024) Vol. 24, Iss. 19, pp. 6237-6237
Open Access | Times Cited: 1
Deep learning based self-adaptive modeling of multimode continuous manufacturing processes and its application to rotary drying process
Tianyu Wang, Ruixiang Zheng, Mian Li, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access
Weighted distributed compressed sensing: An efficient gear transmission system fault feature extraction approach for ultra-low compression signals
Zhongze Liu, Yicong Kuang, Fei Jiang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102833-102833
Closed Access
Prototype space boundary alignment network for bearing continuous fault diagnosis under class-incremental scenarios
Zhenzhong He, Juanjuan Shi, Weiguo Huang, et al.
IEEE Sensors Journal (2024) Vol. 25, Iss. 1, pp. 1076-1085
Closed Access
Spatio-temporal attention-based hidden physics-informed neural network for remaining useful life prediction
F. Jiang, Xiaonan Hou, Min Xia
Advanced Engineering Informatics (2024) Vol. 63, pp. 102958-102958
Open Access
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