Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions
Xiaoan Yan, Daoming She, Yadong Xu
Expert Systems with Applications (2022) Vol. 216, pp. 119479-119479
Closed Access | Times Cited: 68

Showing 1-25 of 68 citing articles:

Wavelet transform for rotary machine fault diagnosis:10 years revisited
Ruqiang Yan, Zuogang Shang, Hong Xu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110545-110545
Closed Access | Times Cited: 100
Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network
Zhuyun Chen, Jingyan Xia, Jipu Li, et al.
Advanced Engineering Informatics (2023) Vol. 57, pp. 102033-102033
Closed Access | Times Cited: 50
Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations
Chao He, Hongmei Shi, Ruixin Li, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102568-102568
Open Access | Times Cited: 17
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
Application of AdaBoost for stator fault diagnosis in three-phase permanent magnet synchronous motors based on vibration–current data fusion analysis
Luttfi A. Al-Haddad, Sameera Sadey Shijer, Alaa Abdulhady Jaber, et al.
Electrical Engineering (2024) Vol. 106, Iss. 4, pp. 4527-4542
Closed Access | Times Cited: 15
IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions
Sheng Li, Jinchen Ji, Yadong Xu, et al.
Reliability Engineering & System Safety (2023) Vol. 237, pp. 109387-109387
Open Access | Times Cited: 33
Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning
Yutong Dong, Hongkai Jiang, Renhe Yao, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109805-109805
Closed Access | Times Cited: 28
Deep continuous convolutional networks for fault diagnosis
Xufeng Huang, Tingli Xie, Jinhong Wu, et al.
Knowledge-Based Systems (2024) Vol. 292, pp. 111623-111623
Closed Access | Times Cited: 14
CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis
Xiaoan Yan, Dong Jiang, Ling Xiang, et al.
Information Fusion (2024) Vol. 112, pp. 102554-102554
Closed Access | Times Cited: 11
Attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for fault diagnosis of rotating machinery
Baosu Guo, Zhaohui Qiao, Ning Zhang, et al.
Expert Systems with Applications (2024) Vol. 249, pp. 123764-123764
Closed Access | Times Cited: 10
Bearing fault diagnosis with parallel CNN and LSTM
Guanghua Fu, Qingjuan Wei, Yongsheng Yang
Mathematical Biosciences & Engineering (2024) Vol. 21, Iss. 2, pp. 2385-2406
Open Access | Times Cited: 9
Spatial correlation learning based on graph neural network for medium-term wind power forecasting
Beizhen Zhao, Xin He, Shaolin Ran, et al.
Energy (2024) Vol. 296, pp. 131164-131164
Closed Access | Times Cited: 9
Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning
Xuemin Cheng, Shuihai Dou, Yanping Du, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced client
Funa Zhou, Shun Liu, Hamido Fujita, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121982-121982
Closed Access | Times Cited: 20
SKND-TSACNN: A novel time-scale adaptive CNN framework for fault diagnosis of rotating machinery
Zidong Yu, Changhe Zhang, Jie Liu, et al.
Knowledge-Based Systems (2023) Vol. 275, pp. 110682-110682
Closed Access | Times Cited: 16
A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance
Suraj Gupta, Akhilesh Kumar, J. Maiti
Safety Science (2024) Vol. 177, pp. 106590-106590
Closed Access | Times Cited: 6
Data-driven machinery fault diagnosis: A comprehensive review
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, et al.
Neurocomputing (2025), pp. 129588-129588
Open Access
A recombination generation method for missing fault data samples under unknown working conditions based on swap invariance-based multi-attribute disentangled representation network
Yujie Cheng, Gaowei Wang, Haoxin Gu, et al.
Measurement (2025), pp. 116982-116982
Closed Access
Semi-Supervised Detection of Structural Damage Using Variational Autoencoder and a One-Class Support Vector Machine
Andrea Pollastro, Giusiana Testa, Antonio Bilotta, et al.
IEEE Access (2023) Vol. 11, pp. 67098-67112
Open Access | Times Cited: 14
Deep Learning in Industrial Machinery: A Critical Review of Bearing Fault Classification Methods
Attiq Ur Rehman, Weidong Jiao, Yonghua Jiang, et al.
Applied Soft Computing (2025), pp. 112785-112785
Closed Access
Dynamic model-assisted disentanglement framework for rolling bearing fault diagnosis under time-varying speed conditions
Yuhui Xu, Yimin Jiang, Tangbin Xia, et al.
Mechanical Systems and Signal Processing (2025) Vol. 230, pp. 112588-112588
Closed Access
Markov-CVAELabeller: A Deep Learning Approach for the Labelling of Fault Data
Christian Velasco-Gallego, Nieves Mateo
Informatics (2025) Vol. 12, Iss. 2, pp. 35-35
Open Access
Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments
Xiaoan Yan, Yanyu LĂĽ, Ying Liu, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 125046-125046
Closed Access | Times Cited: 11
A fault location method based on ensemble complex spatio-temporal attention network for complex systems under fluctuating operating conditions
Jingli Yang, Tianyu Gao, Ge Yan, et al.
Applied Soft Computing (2023) Vol. 144, pp. 110489-110489
Closed Access | Times Cited: 10
Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine
Xin Li, Shuhua Li, Dong Wei, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109882-109882
Closed Access | Times Cited: 10
Page 1 - Next Page

Scroll to top