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:

An explainable unsupervised learning framework for scalable machine fault detection in Industry 4.0
Supriya Asutkar, Siddharth Tallur
Measurement Science and Technology (2023) Vol. 34, Iss. 10, pp. 105123-105123
Closed Access | Times Cited: 6

Showing 6 citing articles:

Impact of interactive learning elements on personal learning performance in immersive virtual reality for construction safety training
Seungwon Seo, Hyunsoo Park, Choongwan Koo
Expert Systems with Applications (2024) Vol. 251, pp. 124099-124099
Open Access | Times Cited: 5

Enhancing Reliability Through Interpretability: A Comprehensive Survey of Interpretable Intelligent Fault Diagnosis in Rotating Machinery
Gang Chen, Junlin Yuan, Yiyue Zhang, et al.
IEEE Access (2024) Vol. 12, pp. 103348-103379
Open Access | Times Cited: 5

A Systematic Review on Advancement of Image Segmentation Techniques for Fault Detection Opportunities and Challenges
Md. Motiur Rahman, Saeka Rahman, Smriti Bhatt, et al.
Electronics (2025) Vol. 14, Iss. 5, pp. 974-974
Open Access

Gaussian-kernel weighted neighborhood preserving embedding algorithm and its application in fault detection
Hancheng Wang, Peng Li, Mingxi Ai, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086207-086207
Closed Access | Times Cited: 3

A self-supervised learning method for fault detection of wind turbines
Shaodan Zhi, Haikuo Shen
Measurement Science and Technology (2024) Vol. 35, Iss. 11, pp. 116118-116118
Closed Access

Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data
Mohammad Noorchenarboo, Katarina Grolinger
Energy and Buildings (2024) Vol. 328, pp. 115177-115177
Open Access

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