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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:

Robust rotating machinery diagnosis using a dynamic-weighted graph updating strategy
Xin Zhang, Youmin Hu, Jie Liu, et al.
Measurement (2022) Vol. 202, pp. 111895-111895
Closed Access | Times Cited: 10

Showing 10 citing articles:

Graph features dynamic fusion learning driven by multi-head attention for large rotating machinery fault diagnosis with multi-sensor data
Xin Zhang, Xi Zhang, Jie Liu, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 125, pp. 106601-106601
Closed Access | Times Cited: 44
A pruned-optimized weighted graph convolutional network for axial flow pump fault diagnosis with hydrophone signals
Xin Zhang, Li Jiang, Lei Wang, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102365-102365
Closed Access | Times Cited: 18
A novel self-supervised representation learning framework based on time-frequency alignment and interaction for mechanical fault diagnosis
Daxing Fu, Jie Liu, Hao Zhong, et al.
Knowledge-Based Systems (2024) Vol. 295, pp. 111846-111846
Closed Access | Times Cited: 10
Multiscale Channel Attention-Driven Graph Dynamic Fusion Learning Method for Robust Fault Diagnosis
Xin Zhang, Jie Liu, Xi Zhang, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 9, pp. 11002-11013
Closed Access | Times Cited: 8
Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery
Lei Wang, Fuchen Xie, Xin Zhang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102711-102711
Closed Access | Times Cited: 6
Triplet adversarial Learning-driven graph architecture search network augmented with Probsparse-attention mechanism for fault diagnosis under Few-shot & Domain-shift
Yuanhong Chang, Jinglong Chen, Weiguang Zheng, et al.
Mechanical Systems and Signal Processing (2023) Vol. 199, pp. 110462-110462
Closed Access | Times Cited: 13
Semi-supervised few-shot fault diagnosis driven by multi-head dynamic graph attention network under speed fluctuations
Li Jiang, Shuaiyu Wang, Tianao Zhang, et al.
Digital Signal Processing (2024) Vol. 151, pp. 104528-104528
Closed Access | Times Cited: 4
Self-supervised graph feature enhancement and scale attention for mechanical signal node-level representation and diagnosis
Xin Zhang, Jie Liu, Xi Zhang, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103197-103197
Closed Access
Entertainment robots based on internet of things and artificial intelligence in the process of student stress and emotion recognition
Zhao Lu, Lingyu Zhou
Entertainment Computing (2024) Vol. 52, pp. 100786-100786
Closed Access | Times Cited: 2
Integration of multi-relational graph oriented fault diagnosis method for nuclear power circulating water pumps
Shuo Zhang, Xintong Ma, Zelin Nie, et al.
Measurement (2024), pp. 115811-115811
Closed Access | Times Cited: 1
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