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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:

Ensemble deep learning-based fault diagnosis of rotor bearing systems
Sai Ma, Fulei Chu
Computers in Industry (2018) Vol. 105, pp. 143-152
Closed Access | Times Cited: 137

Showing 1-25 of 137 citing articles:

A comprehensive review on convolutional neural network in machine fault diagnosis
Jinyang Jiao, Ming Zhao, Jing Lin, et al.
Neurocomputing (2020) Vol. 417, pp. 36-63
Open Access | Times Cited: 407
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Thanasis Kotsiopoulos, Panagiotis Sarigiannidis, Dimosthenis Ioannidis, et al.
Computer Science Review (2021) Vol. 40, pp. 100341-100341
Closed Access | Times Cited: 233
Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review
Mohd Syahril Ramadhan Mohd Saufi, Zair Asrar Ahmad, M. Salman Leong, et al.
IEEE Access (2019) Vol. 7, pp. 122644-122662
Open Access | Times Cited: 226
An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm
Xingqiu Li, Hongkai Jiang, Maogui Niu, et al.
Mechanical Systems and Signal Processing (2020) Vol. 142, pp. 106752-106752
Closed Access | Times Cited: 165
Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform
Pengfei Liang, Chao Deng, Jun Wu, et al.
Computers in Industry (2019) Vol. 113, pp. 103132-103132
Closed Access | Times Cited: 160
A Survey on ensemble learning under the era of deep learning
Yongquan Yang, Haijun Lv, Ning Chen
Artificial Intelligence Review (2022) Vol. 56, Iss. 6, pp. 5545-5589
Closed Access | Times Cited: 149
Evolutionary Machine Learning: A Survey
Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 8, pp. 1-35
Open Access | Times Cited: 142
Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN
Shuzhi Gao, Lintao Xu, Yimin Zhang, et al.
ISA Transactions (2021) Vol. 128, pp. 485-502
Closed Access | Times Cited: 112
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: 67
A literature review of fault diagnosis based on ensemble learning
Zhibao Mian, Xiaofei Deng, Xiaohui Dong, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107357-107357
Open Access | Times Cited: 47
Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)
Anil Kumar, Yuqing Zhou, C.P. Gandhi, et al.
Alexandria Engineering Journal (2020) Vol. 59, Iss. 2, pp. 999-1012
Open Access | Times Cited: 86
Ensemble learning by means of a multi-objective optimization design approach for dealing with imbalanced data sets
Víctor Henrique Alves Ribeiro, Gilberto Reynoso-Meza
Expert Systems with Applications (2020) Vol. 147, pp. 113232-113232
Closed Access | Times Cited: 81
A hybrid of FEM simulations and generative adversarial networks to classify faults in rotor-bearing systems
Yun Gao, Xiaoyang Liu, Haizhou Huang, et al.
ISA Transactions (2020) Vol. 108, pp. 356-366
Closed Access | Times Cited: 80
Deep Ensemble Capsule Network for Intelligent Compound Fault Diagnosis Using Multisensory Data
Ruyi Huang, Jipu Li, Weihua Li, et al.
IEEE Transactions on Instrumentation and Measurement (2019) Vol. 69, Iss. 5, pp. 2304-2314
Closed Access | Times Cited: 79
A Survey on Data-Driven Predictive Maintenance for the Railway Industry
Narjes Davari, Bruno Veloso, Gustavo de Assis Costa, et al.
Sensors (2021) Vol. 21, Iss. 17, pp. 5739-5739
Open Access | Times Cited: 78
Weighted domain adaptation networks for machinery fault diagnosis
Dongdong Wei, Te Han, Fulei Chu, et al.
Mechanical Systems and Signal Processing (2021) Vol. 158, pp. 107744-107744
Closed Access | Times Cited: 76
A novel deep autoencoder and hyperparametric adaptive learning for imbalance intelligent fault diagnosis of rotating machinery
Wanxiang Li, Zhiwu Shang, Maosheng Gao, et al.
Engineering Applications of Artificial Intelligence (2021) Vol. 102, pp. 104279-104279
Closed Access | Times Cited: 57
A novel information processing method based on an ensemble of Auto-Encoders for unsupervised fault detection
Spyridon Plakias, Yiannis S. Boutalis
Computers in Industry (2022) Vol. 142, pp. 103743-103743
Closed Access | Times Cited: 39
Fault diagnosis on the bearing of traction motor in high-speed trains based on deep learning
Yingyong Zou, Yongde Zhang, Hancheng Mao
Alexandria Engineering Journal (2020) Vol. 60, Iss. 1, pp. 1209-1219
Open Access | Times Cited: 69
Self-Supervised Structure Learning for Crack Detection Based on Cycle-Consistent Generative Adversarial Networks
Kaige Zhang, Yingtao Zhang, Heng-Da Cheng
Journal of Computing in Civil Engineering (2020) Vol. 34, Iss. 3
Closed Access | Times Cited: 65
Ensemble learning with member optimization for fault diagnosis of a building energy system
Hua Han, Zhan Zhang, Xiaoyu Cui, et al.
Energy and Buildings (2020) Vol. 226, pp. 110351-110351
Closed Access | Times Cited: 64
Hybrid multimodal fusion with deep learning for rolling bearing fault diagnosis
Changchang Che, Huawei Wang, Xiaomei Ni, et al.
Measurement (2020) Vol. 173, pp. 108655-108655
Closed Access | Times Cited: 63
Rolling bearing fault diagnosis based on feature fusion with parallel convolutional neural network
Mingxuan Liang, Pei Cao, Jiong Tang
The International Journal of Advanced Manufacturing Technology (2020) Vol. 112, Iss. 3-4, pp. 819-831
Closed Access | Times Cited: 62
Ensemble 1-D CNN diagnosis model for VRF system refrigerant charge faults under heating condition
Heng-Da Cheng, Huanxin Chen, Zhengfei Li, et al.
Energy and Buildings (2020) Vol. 224, pp. 110256-110256
Closed Access | Times Cited: 57
Fault Diagnosis of a Rotor-Bearing System Under Variable Rotating Speeds Using Two-Stage Parameter Transfer and Infrared Thermal Images
Haidong Shao, Wei Li, Min Xia, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 52
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