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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 hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery
Xinya Wu, Yan Zhang, Changming Cheng, et al.
Mechanical Systems and Signal Processing (2020) Vol. 149, pp. 107327-107327
Closed Access | Times Cited: 185

Showing 1-25 of 185 citing articles:

A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhiqin Zhu, Yangbo Lei, Guanqiu Qi, et al.
Measurement (2022) Vol. 206, pp. 112346-112346
Closed Access | Times Cited: 281
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: 148
Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis
Zhang We, Ziwei Wang, Xiang Li
Reliability Engineering & System Safety (2022) Vol. 229, pp. 108885-108885
Closed Access | Times Cited: 113
Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations
Kai Zhou, Edward Diehl, Jiong Tang
Mechanical Systems and Signal Processing (2022) Vol. 185, pp. 109772-109772
Open Access | Times Cited: 106
A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms
Jian Cen, Zhuohong Yang, Xi Liu, et al.
Journal of Vibration Engineering & Technologies (2022) Vol. 10, Iss. 7, pp. 2481-2507
Closed Access | Times Cited: 87
Transfer learning based on improved stacked autoencoder for bearing fault diagnosis
Shuyang Luo, Xufeng Huang, Yanzhi Wang, et al.
Knowledge-Based Systems (2022) Vol. 256, pp. 109846-109846
Closed Access | Times Cited: 72
TFN: An interpretable neural network with time-frequency transform embedded for intelligent fault diagnosis
Qian Chen, Xingjian Dong, Guowei Tu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 207, pp. 110952-110952
Open Access | Times Cited: 57
Detection model transfer of apple soluble solids content based on NIR spectroscopy and deep learning
Zhiming Guo, Yiyin Zhang, Junyi Wang, et al.
Computers and Electronics in Agriculture (2023) Vol. 212, pp. 108127-108127
Closed Access | Times Cited: 52
A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern
Pengcheng Xia, Yixiang Huang, Zhiyu Tao, et al.
Reliability Engineering & System Safety (2023) Vol. 235, pp. 109256-109256
Closed Access | Times Cited: 42
A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings
Haoran Wen, Wei Guo, Xiang Li
Expert Systems with Applications (2023) Vol. 225, pp. 120066-120066
Closed Access | Times Cited: 39
A new multi-source information domain adaption network based on domain attributes and features transfer for cross-domain fault diagnosis
Yue Yu, Hamid Reza Karimi, Peiming Shi, et al.
Mechanical Systems and Signal Processing (2024) Vol. 211, pp. 111194-111194
Open Access | Times Cited: 20
Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis
Zhuang Ye, Jianbo Yu
Mechanical Systems and Signal Processing (2021) Vol. 161, pp. 107984-107984
Closed Access | Times Cited: 84
Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data
Huan Wang, Zhiliang Liu, Yipei Ge, et al.
Knowledge-Based Systems (2021) Vol. 239, pp. 107978-107978
Closed Access | Times Cited: 56
Research on bearing fault diagnosis method based on transformer neural network
Zhuohong Yang, Jian Cen, Xi Liu, et al.
Measurement Science and Technology (2022) Vol. 33, Iss. 8, pp. 085111-085111
Closed Access | Times Cited: 44
An unsupervised chatter detection method based on AE and merging GMM and K-means
Bo Liu, Changfu Liu, Zhou Yang, et al.
Mechanical Systems and Signal Processing (2022) Vol. 186, pp. 109861-109861
Closed Access | Times Cited: 42
A novel multi-source sensing data fusion driven method for detecting rolling mill health states under imbalanced and limited datasets
Peiming Shi, Yue Yu, Hao Gao, et al.
Mechanical Systems and Signal Processing (2022) Vol. 171, pp. 108903-108903
Closed Access | Times Cited: 41
Mutual-assistance semisupervised domain generalization network for intelligent fault diagnosis under unseen working conditions
Chao Zhao, Weiming Shen
Mechanical Systems and Signal Processing (2022) Vol. 189, pp. 110074-110074
Closed Access | Times Cited: 40
Semi-supervised fault diagnosis of machinery using LPS-DGAT under speed fluctuation and extremely low labeled rates
Shen Yan, Haidong Shao, Yiming Xiao, et al.
Advanced Engineering Informatics (2022) Vol. 53, pp. 101648-101648
Closed Access | Times Cited: 39
Semi-supervised learning for industrial fault detection and diagnosis: A systemic review
José Miguel Ramírez‐Sanz, Jose-Alberto Maestro-Prieto, Álvar Arnaiz‐González, et al.
ISA Transactions (2023) Vol. 143, pp. 255-270
Open Access | Times Cited: 36
Fault Diagnosis using eXplainable AI: A transfer learning-based approach for rotating machinery exploiting augmented synthetic data
Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito, et al.
Expert Systems with Applications (2023) Vol. 232, pp. 120860-120860
Open Access | Times Cited: 35
Multi-sensor information fusion and coordinate attention-based fault diagnosis method and its interpretability research
Jinyu Tong, Cang Liu, Jinde Zheng, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106614-106614
Open Access | Times Cited: 35
LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)
Jae Seok, Akeem Bayo Kareem, Jang-Wook Hur
Sensors (2023) Vol. 23, Iss. 2, pp. 1009-1009
Open Access | Times Cited: 32
Semi-Supervised Contrast Learning Based on Multiscale Attention and Multitarget Contrast Learning for Bearing Fault Diagnosis
Weiwei Zhang, Deji Chen, Yang Xiao, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 19, Iss. 10, pp. 10056-10068
Closed Access | Times Cited: 29
Machine learning for fault analysis in rotating machinery: A comprehensive review
Oğuzhan Daş, Duygu Bağcı Daş, Derya Birant
Heliyon (2023) Vol. 9, Iss. 6, pp. e17584-e17584
Open Access | Times Cited: 29
Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery
Mingkuan Shi, Chuancang Ding, Rui Wang, et al.
Reliability Engineering & System Safety (2023) Vol. 240, pp. 109601-109601
Closed Access | Times Cited: 29
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