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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:

Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism
Xiang Li, Zhang We, Qian Ding
Signal Processing (2019) Vol. 161, pp. 136-154
Closed Access | Times Cited: 323

Showing 1-25 of 323 citing articles:

Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Zhibin Zhao, Tianfu Li, Jingyao Wu, et al.
ISA Transactions (2020) Vol. 107, pp. 224-255
Open Access | Times Cited: 426
Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning
Xiang Li, Zhang We, Qian Ding, et al.
IEEE Transactions on Industrial Informatics (2019) Vol. 16, Iss. 3, pp. 1688-1697
Closed Access | Times Cited: 254
A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings
Yifei Ding, Minping Jia, Qiuhua Miao, et al.
Mechanical Systems and Signal Processing (2021) Vol. 168, pp. 108616-108616
Open Access | Times Cited: 245
Federated learning for machinery fault diagnosis with dynamic validation and self-supervision
Zhang We, Xiang Li, Hui Ma, et al.
Knowledge-Based Systems (2020) Vol. 213, pp. 106679-106679
Closed Access | Times Cited: 224
An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery
Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito, et al.
Mechanical Systems and Signal Processing (2021) Vol. 163, pp. 108105-108105
Open Access | Times Cited: 215
A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems
Ting Huang, Qiang Zhang, Xiaoan Tang, et al.
Artificial Intelligence Review (2021) Vol. 55, Iss. 2, pp. 1289-1315
Closed Access | Times Cited: 164
Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation
Zhang We, Xiang Li, Xu Li
Measurement (2020) Vol. 164, pp. 108052-108052
Closed Access | Times Cited: 162
Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis
Yong Feng, Jinglong Chen, Tianci Zhang, et al.
ISA Transactions (2021) Vol. 120, pp. 383-401
Closed Access | Times Cited: 158
Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application
Haixin Lv, Jinglong Chen, Tongyang Pan, et al.
Measurement (2022) Vol. 199, pp. 111594-111594
Closed Access | Times Cited: 148
A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox
Kai Zhang, Baoping Tang, Lei Deng, et al.
Measurement (2021) Vol. 179, pp. 109491-109491
Closed Access | Times Cited: 144
Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method
Jun Li, Yongbao Liu, Qijie Li
Measurement (2021) Vol. 189, pp. 110500-110500
Closed Access | Times Cited: 143
A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
Mohammed Hakim, Abdoulhdi A. Borhana Omran, Ali Najah Ahmed, et al.
Ain Shams Engineering Journal (2022) Vol. 14, Iss. 4, pp. 101945-101945
Open Access | Times Cited: 129
A deep feature enhanced reinforcement learning method for rolling bearing fault diagnosis
Ruixin Wang, Hongkai Jiang, Ke Zhu, et al.
Advanced Engineering Informatics (2022) Vol. 54, pp. 101750-101750
Closed Access | Times Cited: 91
Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis
Shuo Zhang, Zhiwen Liu, Yunping Chen, et al.
ISA Transactions (2022) Vol. 133, pp. 369-383
Closed Access | Times Cited: 84
Rolling Bearing Fault Diagnosis Method Base on Periodic Sparse Attention and LSTM
Yiyao An, Ke Zhang, Qie Liu, et al.
IEEE Sensors Journal (2022) Vol. 22, Iss. 12, pp. 12044-12053
Closed Access | Times Cited: 76
Physics-informed interpretable wavelet weight initialization and balanced dynamic adaptive threshold for intelligent fault diagnosis of rolling bearings
Chao He, Hongmei Shi, Jin Si, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 579-592
Closed Access | Times Cited: 59
A multi-stage data augmentation and AD-ResNet-based method for EPB utilization factor prediction
Honggan Yu, Hao Sun, Jianfeng Tao, et al.
Automation in Construction (2023) Vol. 147, pp. 104734-104734
Closed Access | Times Cited: 51
A transformer-based approach for novel fault detection and fault classification/diagnosis in manufacturing: A rotary system application
Haiyue Wu, Matthew J. Triebe, John W. Sutherland
Journal of Manufacturing Systems (2023) Vol. 67, pp. 439-452
Closed Access | Times Cited: 51
A review of high-definition map creation methods for autonomous driving
Zhibin Bao, Sabir Hossain, Haoxiang Lang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106125-106125
Closed Access | Times Cited: 41
Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method
Lijun Zhang, Tuo Ji, Shihao Yu, et al.
Batteries (2023) Vol. 9, Iss. 3, pp. 177-177
Open Access | Times Cited: 40
Deep Learning-Based Bearing Fault Diagnosis Using a Trusted Multiscale Quadratic Attention-Embedded Convolutional Neural Network
Yuheng Tang, Chaoyong Zhang, Jianzhao Wu, et al.
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-15
Closed Access | Times Cited: 19
A residual denoising and multiscale attention-based weighted domain adaptation network for tunnel boring machine main bearing fault diagnosis
Zhong Tao, Chengjin Qin, Gang Shi, et al.
Science China Technological Sciences (2024) Vol. 67, Iss. 8, pp. 2594-2618
Closed Access | Times Cited: 18
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring
Yuandi Wu, Brett Sicard, S. Andrew Gadsden
Expert Systems with Applications (2024) Vol. 255, pp. 124678-124678
Open Access | Times Cited: 15
Domain generalization in rotating machinery fault diagnostics using deep neural networks
Xiang Li, Zhang We, Hui Ma, et al.
Neurocomputing (2020) Vol. 403, pp. 409-420
Closed Access | Times Cited: 126
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