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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 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

Showing 1-25 of 245 citing articles:

Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework
Taotao Zhou, Te Han, Enrique López Droguett
Reliability Engineering & System Safety (2022) Vol. 224, pp. 108525-108525
Closed Access | Times Cited: 153
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
Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions
Yifei Ding, Minping Jia, Jichao Zhuang, et al.
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108890-108890
Closed Access | Times Cited: 125
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Bayu Adhi Tama, Malinda Vania, Seung‐Chul Lee, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 5, pp. 4667-4709
Open Access | Times Cited: 120
Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer
Yiming Xiao, Haidong Shao, Minjie Feng, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 186-201
Open Access | Times Cited: 108
Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis
Yiming Xiao, Haidong Shao, Jie Wang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 207, pp. 110936-110936
Open Access | Times Cited: 103
LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention
Shen Yan, Haidong Shao, Jie Wang, et al.
Expert Systems with Applications (2023) Vol. 237, pp. 121338-121338
Open Access | Times Cited: 95
Diagnosisformer: An efficient rolling bearing fault diagnosis method based on improved Transformer
Yandong Hou, Jinjin Wang, Zhengquan Chen, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106507-106507
Closed Access | Times Cited: 79
Supervised Contrastive Learning-Based Domain Adaptation Network for Intelligent Unsupervised Fault Diagnosis of Rolling Bearing
Yongchao Zhang, Zhaohui Ren, Shihua Zhou, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 27, Iss. 6, pp. 5371-5380
Closed Access | Times Cited: 76
Convformer-NSE: A Novel End-to-End Gearbox Fault Diagnosis Framework Under Heavy Noise Using Joint Global and Local Information
Song-Yu Han, Haidong Shao, Junsheng Cheng, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 28, Iss. 1, pp. 340-349
Closed Access | Times Cited: 75
Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network
Pengfei Liang, Zhuoze Yu, Bin Wang, et al.
Advanced Engineering Informatics (2023) Vol. 57, pp. 102075-102075
Closed Access | Times Cited: 70
Transfer Learning for Remaining Useful Life Prediction Across Operating Conditions Based on Multisource Domain Adaptation
Yifei Ding, Peng Ding, Xiaoli Zhao, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 27, Iss. 5, pp. 4143-4152
Closed Access | Times Cited: 67
Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review
Shaohua Qiu, Xiaopeng Cui, Zuowei Ping, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1305-1305
Open Access | Times Cited: 65
Spatial correlation and temporal attention-based LSTM for remaining useful life prediction of turbofan engine
Huixin Tian, Linzheng Yang, Bingtian Ju
Measurement (2023) Vol. 214, pp. 112816-112816
Closed Access | Times Cited: 61
A hybrid prognosis scheme for rolling bearings based on a novel health indicator and nonlinear Wiener process
Junyu Guo, Zhiyuan Wang, He Li, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 110014-110014
Open Access | Times Cited: 47
Non-contact diagnosis for gearbox based on the fusion of multi-sensor heterogeneous data
Dingyi Sun, Yongbo Li, Sixiang Jia, et al.
Information Fusion (2023) Vol. 94, pp. 112-125
Closed Access | Times Cited: 42
Noise-boosted weak signal detection in fractional nonlinear systems enhanced by increasing potential-well width and its application to mechanical fault diagnosis
Zijian Qiao, Yuanbiao He, Changrong Liao, et al.
Chaos Solitons & Fractals (2023) Vol. 175, pp. 113960-113960
Closed Access | Times Cited: 39
Neural-transformer: A brain-inspired lightweight mechanical fault diagnosis method under noise
Changdong Wang, Bowen Tian, Jingli Yang, et al.
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110409-110409
Closed Access | Times Cited: 38
Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM
You Keshun, Wang Puzhou, Yingkui Gu
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 23002-23019
Closed Access | Times Cited: 32
Rolling bearing fault diagnosis method based on improved residual shrinkage network
Linjun Wang, Tengxiao Zou, Kanglin Cai, et al.
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2024) Vol. 46, Iss. 3
Closed Access | Times Cited: 25
Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis
Yutong Dong, Hongkai Jiang, Mingzhe Mu, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102573-102573
Closed Access | Times Cited: 22
TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network
Zhaoyang Fu, Zheng Liu, Shuangrui Ping, et al.
ISA Transactions (2024) Vol. 149, pp. 381-393
Closed Access | Times Cited: 21
A multi-sensor fused incremental broad learning with D-S theory for online fault diagnosis of rotating machinery
Xuefang Xu, Shuo Bao, Haidong Shao, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102419-102419
Closed Access | Times Cited: 17
Attention on the key modes: Machinery fault diagnosis transformers through variational mode decomposition
Hebin Liu, Qizhi Xu, Xiaolin Han, et al.
Knowledge-Based Systems (2024) Vol. 289, pp. 111479-111479
Closed Access | Times Cited: 16
Application of deep learning to fault diagnosis of rotating machineries
Hao Su, Ling Xiang, Aijun Hu
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 042003-042003
Open Access | Times Cited: 15
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