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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 Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains
Dandan Peng, Zhiliang Liu, Huan Wang, et al.
IEEE Access (2018) Vol. 7, pp. 10278-10293
Open Access | Times Cited: 224

Showing 26-50 of 224 citing articles:

A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles
Guangxi Yan, Chen Jiang, Yu Bai, et al.
Processes (2022) Vol. 10, Iss. 4, pp. 724-724
Open Access | Times Cited: 44
Fault diagnosis method of rolling bearings based on adaptive modified CEEMD and 1DCNN model
Shuzhi Gao, Tianchi Li, Yimin Zhang, et al.
ISA Transactions (2023) Vol. 140, pp. 309-330
Closed Access | Times Cited: 32
An interpretable convolutional neural network with multi-wavelet kernel fusion for intelligent fault diagnosis
Guoqian Jiang, Jing Wang, Lijin Wang, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 18-30
Closed Access | Times Cited: 31
Knowledge-informed deep networks for robust fault diagnosis of rolling bearings
Yunsheng Su, Luojie Shi, Kai Zhou, et al.
Reliability Engineering & System Safety (2023) Vol. 244, pp. 109863-109863
Closed Access | Times Cited: 29
Bearing Fault Classification Based on Convolutional Neural Network in Noise Environment
Qinyu Jiang, Faliang Chang, Bowen Sheng
IEEE Access (2019) Vol. 7, pp. 69795-69807
Open Access | Times Cited: 67
Multivariate Abnormal Detection for Industrial Control Systems Using 1D CNN and GRU
Xin Xie, Bin Wang, Tiancheng Wan, et al.
IEEE Access (2020) Vol. 8, pp. 88348-88359
Open Access | Times Cited: 67
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
Huihui Qiao, Wang Tai-yong, Peng Wang, et al.
IEEE Access (2019) Vol. 7, pp. 118954-118964
Open Access | Times Cited: 60
A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures
Shoucong Xiong, Hongdi Zhou, Shuai He, et al.
Sensors (2020) Vol. 20, Iss. 17, pp. 4965-4965
Open Access | Times Cited: 56
Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network
Rafia Nishat Toma, Cheol Hong Kim, Jong-Myon Kim
Electronics (2021) Vol. 10, Iss. 11, pp. 1248-1248
Open Access | Times Cited: 53
Bogie fault diagnosis under variable operating conditions based on fast kurtogram and deep residual learning towards imbalanced data
Yixuan Geng, Zhipeng Wang, Limin Jia, et al.
Measurement (2020) Vol. 166, pp. 108191-108191
Closed Access | Times Cited: 51
Unsupervised deep transfer learning with moment matching: A new intelligent fault diagnosis approach for bearings
Jin Si, Hongmei Shi, Jingcheng Chen, et al.
Measurement (2020) Vol. 172, pp. 108827-108827
Closed Access | Times Cited: 51
Bearing Fault Diagnosis Based on Multiple Transformation Domain Fusion and Improved Residual Dense Networks
Jiedi Sun, Jiangtao Wen, Caiyan Yuan, et al.
IEEE Sensors Journal (2021) Vol. 22, Iss. 2, pp. 1541-1551
Closed Access | Times Cited: 40
Long-range Dependencies Learning Based on Non-Local 1D-Convolutional Neural Network for Rolling Bearing Fault Diagnosis
Huan Wang, Zhiliang Liu, Ting Ai
Journal of Dynamics Monitoring and Diagnostics (2022), pp. 148-159
Open Access | Times Cited: 30
Knowledge transfer in fault diagnosis of rotary machines
Guokai Liu, Weiming Shen, Liang Gao, et al.
IET Collaborative Intelligent Manufacturing (2022) Vol. 4, Iss. 1, pp. 17-34
Open Access | Times Cited: 29
One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram
Manali Saini, Udit Satija, Madhur Deo Upadhayay
Biomedical Signal Processing and Control (2022) Vol. 74, pp. 103494-103494
Open Access | Times Cited: 28
GMA-DRSNs: A novel fault diagnosis method with global multi-attention deep residual shrinkage networks
Zhijin Zhang, Lei Chen, Chunlei Zhang, et al.
Measurement (2022) Vol. 196, pp. 111203-111203
Closed Access | Times Cited: 28
Fault diagnosis and self-healing for smart manufacturing: a review
Joma Aldrini, Inès Chihi, Lilia Sidhom
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 6, pp. 2441-2473
Open Access | Times Cited: 20
Fault Diagnosis Method and Application Based on Multi-scale Neural Network and Data Enhancement for Strong Noise
Zhehui Shao, Wenqiang Li, Hai Xiang, et al.
Journal of Vibration Engineering & Technologies (2023) Vol. 12, Iss. 1, pp. 295-308
Closed Access | Times Cited: 19
Axle bearing fault diagnosis for high-speed trains: A comprehensive review of methodologies, technologies, challenges and emerging trends
Meryem Abtane, Khalid Dahi, Hervé Martinez, et al.
Measurement (2025) Vol. 251, pp. 117098-117098
Closed Access
A Newly Designed Diagnostic Method for Mechanical Faults of High-Voltage Circuit Breakers via SSAE and IELM
Wei Gao, Su-Peng Qiao, Rong‐Jong Wai, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 70, pp. 1-13
Closed Access | Times Cited: 40
A novel multi-scale convolution model based on multi-dilation rates and multi-attention mechanism for mechanical fault diagnosis
Caiyuan Chu, Yongxin Ge, Quan Qian, et al.
Digital Signal Processing (2021) Vol. 122, pp. 103355-103355
Closed Access | Times Cited: 33
Hybrid deep transfer learning architecture for industrial fault diagnosis using Hilbert transform and DCNN–LSTM
Mahe Zabin, Ho‐Jin Choi, Jia Uddin
The Journal of Supercomputing (2022) Vol. 79, Iss. 5, pp. 5181-5200
Open Access | Times Cited: 27
An attention-enhanced multi-modal deep learning algorithm for robotic compound fault diagnosis
Xing Zhou, Hanlin Zeng, Chong Chen, et al.
Measurement Science and Technology (2022) Vol. 34, Iss. 1, pp. 014007-014007
Closed Access | Times Cited: 22
Machine learning for fault diagnosis of high-speed train traction systems: A review
Huan Wang, Yan‐Fu Li, Jianliang Ren
Frontiers of Engineering Management (2023) Vol. 11, Iss. 1, pp. 62-78
Closed Access | Times Cited: 14
Fourier Feature Refiner Network With Soft Thresholding for Machinery Fault Diagnosis Under Highly Noisy Conditions
Huan Wang, Wenjun Luo, Zhiliang Liu, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 22880-22891
Closed Access | Times Cited: 4

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