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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.

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Showing 14 citing articles:

Fault diagnosis in reciprocating air compressors using ensemble voting classifier
S.R.S. Reddy, Naveen Venkatesh Sridharan, S. Aravinth, et al.
Structural Health Monitoring (2025)
Closed Access
Variational time–frequency adaptive decomposition of machine multi-impact vibration signals
Nanyang Zhao, Jinjie Zhang, Zhiwei Mao, et al.
Mechanical Systems and Signal Processing (2022) Vol. 189, pp. 110084-110084
Closed Access | Times Cited: 21
A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine
Xiaoan Yan, Ying Liu, Minping Jia
Sensors (2020) Vol. 20, Iss. 15, pp. 4352-4352
Open Access | Times Cited: 28
Fault diagnosis of air compressors using transfer learning: A comparative study of pre-trained networks and hyperparameter optimization
B Srivatsan, Naveen Venkatesh Sridharan, S. Aravinth, et al.
Journal of low frequency noise, vibration and active control (2024) Vol. 43, Iss. 4, pp. 1877-1894
Open Access | Times Cited: 3
Quantitative diagnosis of loose piston rod threads in reciprocating compressors for hydrogen storage and transport
Xueying Li, Anna Diao, Yi Guo, et al.
International Journal of Hydrogen Energy (2023) Vol. 48, Iss. 94, pp. 37013-37030
Closed Access | Times Cited: 8
Approach to the Quantitative Diagnosis of Rolling Bearings Based on Optimized VMD and Lempel–Ziv Complexity under Varying Conditions
Haobo Wang, Tongguang Yang, Qingkai Han, et al.
Sensors (2023) Vol. 23, Iss. 8, pp. 4044-4044
Open Access | Times Cited: 5
Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method
Liubang Han, Jianglong Li, Qidong Wang, et al.
Applied Sciences (2020) Vol. 10, Iss. 6, pp. 1946-1946
Open Access | Times Cited: 11
Prediction of air compressor condition using vibration signals and machine learning algorithms
S. Aravinth, V. Sugumaran
Journal of Vibration and Control (2022) Vol. 29, Iss. 5-6, pp. 1342-1351
Closed Access | Times Cited: 4
Experimental Study on the Identification and Diagnosis of Dynamic Crack Propagation in the Piston Rods of Process Gas Compressors in Underground Gas Storage
Xueying Li, Z. H. Chen, Shuang Wu, et al.
Applied Sciences (2024) Vol. 14, Iss. 2, pp. 857-857
Open Access
Rolling Bearing Fault Diagnosis Using Deep Transfer Learning Based on Joint Generalized Sliced Wasserstein Distance
Na Lei, Jipeng Cui, Jicheng Han, et al.
IEEE Access (2024) Vol. 12, pp. 41452-41463
Open Access
Transfer Learning-Based Fault Diagnosis of Single-Stage Single-Acting Air Compressor
Ganjikunta Chakrapani, Naveen Venkatesh Sridharan, S. Aravinth, et al.
Journal of Vibration Engineering & Technologies (2023) Vol. 12, Iss. 3, pp. 4411-4428
Closed Access | Times Cited: 1
A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction
Weifeng Wu, Chengyi Li, Zhongqing Zhu, et al.
Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy (2023) Vol. 237, Iss. 6, pp. 1337-1347
Closed Access | Times Cited: 1
Research on fault diagnosis method of reciprocating compressor valve based on IVMD-CMS model
Fengfeng Bie, Suzhen Chen, Fengxia Lyu, et al.
Journal of Mechanical Science and Technology (2023) Vol. 37, Iss. 8, pp. 3931-3943
Closed Access | Times Cited: 1
Compressor Fault Diagnosis Employing Machine Learning Techniques
Kalathil Prathapan Pushya, P. Supriya, Sasidharan Sreedharan
2019 Innovations in Power and Advanced Computing Technologies (i-PACT) (2023), pp. 1-8
Closed Access | Times Cited: 1
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