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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:

End-to-end unsupervised fault detection using a flow-based model
Liangwei Zhang, Jing Lin, Haidong Shao, et al.
Reliability Engineering & System Safety (2021) Vol. 215, pp. 107805-107805
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework
Wei Li, Xiang Zhong, Haidong Shao, et al.
Advanced Engineering Informatics (2022) Vol. 52, pp. 101552-101552
Closed Access | Times Cited: 216
Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings
Yifei Ding, Jichao Zhuang, Peng Ding, et al.
Reliability Engineering & System Safety (2021) Vol. 218, pp. 108126-108126
Closed Access | Times Cited: 153
Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions
Yadong Xu, Xiaoan Yan, Beibei Sun, et al.
Reliability Engineering & System Safety (2022) Vol. 225, pp. 108618-108618
Closed Access | Times Cited: 74
A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis
Shuaiyu Zhao, Yiling Duan, Nitin Roy, et al.
Reliability Engineering & System Safety (2024) Vol. 249, pp. 110208-110208
Closed Access | Times Cited: 19
A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions
Liangwei Zhang, Qi Fan, Jing Lin, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 119, pp. 105735-105735
Closed Access | Times Cited: 66
Contrastive self-supervised learning: review, progress, challenges and future research directions
Pranjal Kumar, Piyush Rawat, Siddhartha Chauhan
International Journal of Multimedia Information Retrieval (2022) Vol. 11, Iss. 4, pp. 461-488
Closed Access | Times Cited: 40
Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines
Zhen Chen, Di Zhou, Enrico Zio, et al.
Reliability Engineering & System Safety (2023) Vol. 234, pp. 109162-109162
Closed Access | Times Cited: 29
Dually attentive multiscale networks for health state recognition of rotating machinery
Yadong Xu, Xiaoan Yan, Beibei Sun, et al.
Reliability Engineering & System Safety (2022) Vol. 225, pp. 108626-108626
Closed Access | Times Cited: 29
Machine learning for prognostics and health management of industrial mechanical systems and equipment: A systematic literature review
Lorenzo Polverino, Raffaele Abbate, Pasquale Manco, et al.
International Journal of Engineering Business Management (2023) Vol. 15
Open Access | Times Cited: 16
A component diagnostic and prognostic framework for pump bearings based on deep learning with data augmentation
Andy Rivas, Gregory Delipei, Ian J. Davis, et al.
Reliability Engineering & System Safety (2024) Vol. 247, pp. 110121-110121
Closed Access | Times Cited: 7
MCBA-MVACGAN: A Novel Fault Diagnosis Method for Rotating Machinery Under Small Sample Conditions
Wenhan Huang, Xiangfeng Zhang, Hong Jiang, et al.
Machines (2025) Vol. 13, Iss. 1, pp. 71-71
Open Access
Data augmentation on fault diagnosis of wind turbine gearboxes with an enhanced flow-based generative model
Wenliao Du, Pengxiang Zhu, Ziqiang Pu, et al.
Measurement (2023) Vol. 225, pp. 113985-113985
Closed Access | Times Cited: 12
Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning
Jiahui He, Zhijun Cheng, Bo Guo
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109717-109717
Closed Access | Times Cited: 11
Mining knowledge from unlabeled data for fault diagnosis: A multi-task self-supervised approach
Depeng Kong, Weidi Huang, Libo Zhao, et al.
Mechanical Systems and Signal Processing (2024) Vol. 211, pp. 111189-111189
Closed Access | Times Cited: 4
A self-supervised leak detection method for natural gas gathering pipelines considering unlabeled multi-class non-leak data
Zhonglin Zuo, Hao Zhang, Zheng Li, et al.
Computers in Industry (2024) Vol. 159-160, pp. 104102-104102
Closed Access | Times Cited: 3
A novel model-independent data augmentation method for fault diagnosis in smart manufacturing
Pin Lyu, Hanbin Zhang, Wenbing Yu, et al.
Procedia CIRP (2022) Vol. 107, pp. 949-954
Open Access | Times Cited: 13
Dual-loss nonlinear independent component estimation for augmenting explainable vibration samples of rotating machinery faults
Xiaoyun Gong, Mengxuan Hao, Chuan Li, et al.
Neurocomputing (2024) Vol. 610, pp. 128508-128508
Closed Access | Times Cited: 2
An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery
Long Zhang, Yangyuan Liu, Jianmin Zhou, et al.
Sensors (2022) Vol. 22, Iss. 22, pp. 8749-8749
Open Access | Times Cited: 10
An unsupervised spatiotemporal fusion network augmented with random mask and time-relative information modulation for anomaly detection of machines with multiple measuring points
Kaiyu Zhang, Jinglong Chen, Chi-Guhn Lee, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121506-121506
Closed Access | Times Cited: 5
Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment
Yifei Ding, Minping Jia, Yudong Cao, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 20, Iss. 2, pp. 1313-1323
Closed Access | Times Cited: 4
Fault detection and classification with the rebmix R package
Marko Nagode, Branislav Panić, Jernej Klemenc, et al.
Computers & Industrial Engineering (2023) Vol. 185, pp. 109628-109628
Open Access | Times Cited: 4
Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
Liangwei Zhang, Junyan Zhang, Yeping Peng, et al.
Applied Sciences (2022) Vol. 12, Iss. 14, pp. 7032-7032
Open Access | Times Cited: 4
Wave-ConvNeXt: An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform
Liangwei Zhang, Jing Lin, Zhe Yang, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 23096-23109
Open Access
A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis
shubo yu, Zhansheng Liu, Saibo Wang, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086133-086133
Closed Access
Enhanced Data Augmentation for Bearing Fault Diagnosis by Using a Spectral Flow Model
Xiaoyun Gong, Mengxuan Hao, Chuan Li, et al.
(2024), pp. 405-409
Closed Access
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