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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:

Constructing a health indicator for roller bearings by using a stacked auto-encoder with an exponential function to eliminate concussion
Fan Xu, Zhelin Huang, Fangfang Yang, et al.
Applied Soft Computing (2020) Vol. 89, pp. 106119-106119
Closed Access | Times Cited: 64

Showing 1-25 of 64 citing articles:

The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Tianfu Li, Zheng Zhou, Sinan Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 168, pp. 108653-108653
Closed Access | Times Cited: 302
Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors
Han Cheng, Xianguang Kong, Gaige Chen, et al.
Measurement (2020) Vol. 168, pp. 108286-108286
Closed Access | Times Cited: 166
A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
Omar AlShorman, Muhammad Irfan, Nordin Saad, et al.
Shock and Vibration (2020) Vol. 2020, pp. 1-20
Open Access | Times Cited: 159
A novel health indicator for intelligent prediction of rolling bearing remaining useful life based on unsupervised learning model
Zifei Xu, Musa Bashir, Qinsong Liu, et al.
Computers & Industrial Engineering (2023) Vol. 176, pp. 108999-108999
Open Access | Times Cited: 56
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: 45
Temporal multi-resolution hypergraph attention network for remaining useful life prediction of rolling bearings
Jinxin Wu, Deqiang He, Jiayi Li, et al.
Reliability Engineering & System Safety (2024) Vol. 247, pp. 110143-110143
Closed Access | Times Cited: 27
Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction
Dingliang Chen, Yi Qin, Yi Wang, et al.
ISA Transactions (2020) Vol. 114, pp. 44-56
Closed Access | Times Cited: 128
An intelligent fault diagnosis method for rotor-bearing system using small labeled infrared thermal images and enhanced CNN transferred from CAE
Zhiyi He, Haidong Shao, Zhong Xiang, et al.
Advanced Engineering Informatics (2020) Vol. 46, pp. 101150-101150
Closed Access | Times Cited: 105
Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds
Xin Li, Haidong Shao, Hongkai Jiang, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 2, pp. 339-353
Closed Access | Times Cited: 66
Health indicator for machine condition monitoring built in the latent space of a deep autoencoder
Ana González, Ignacio Díaz, Abel A. Cuadrado, et al.
Reliability Engineering & System Safety (2022) Vol. 224, pp. 108482-108482
Open Access | Times Cited: 63
IMS-CDA: Prediction of CircRNA-Disease Associations From the Integration of Multisource Similarity Information With Deep Stacked Autoencoder Model
Lei Wang, Zhu‐Hong You, Jianqiang Li, et al.
IEEE Transactions on Cybernetics (2020) Vol. 51, Iss. 11, pp. 5522-5531
Closed Access | Times Cited: 57
RUL Prediction of Rolling Bearings Based on a DCAE and CNN
Chenyang Wang, Wanlu Jiang, Xukang Yang, et al.
Applied Sciences (2021) Vol. 11, Iss. 23, pp. 11516-11516
Open Access | Times Cited: 43
Transfer life prediction of gears by cross-domain health indicator construction and multi-hierarchical long-term memory augmented network
Dingliang Chen, Yi Qin, Quan Qian, et al.
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108916-108916
Closed Access | Times Cited: 27
Multiscale reduction clustering of vibration signals for unsupervised diagnosis of machine faults
Yifan Wu, Chuan Li, Shuai Yang, et al.
Applied Soft Computing (2023) Vol. 142, pp. 110358-110358
Closed Access | Times Cited: 18
Online prognostic failure AIoT system for industrial generators maintenance service based two-stage deep learning algorithm
D. T. Nguyen, Phuong Nguyen Thanh, Ming-Yuan Cho
Control Engineering Practice (2025) Vol. 157, pp. 106263-106263
Closed Access
An intelligent data-driven adaptive health state assessment approach for rolling bearings under single and multiple working conditions
Wenqin Zhao, Yaqiong Lv, C.K.M. Lee, et al.
Computers & Industrial Engineering (2025), pp. 110988-110988
Closed Access
A method for predicting the remaining useful life of rolling bearings under different working conditions based on multi-domain adversarial networks
Yisheng Zou, Zhixuan Li, Yongzhi Liu, et al.
Measurement (2021) Vol. 188, pp. 110393-110393
Closed Access | Times Cited: 36
Pump remaining useful life prediction based on multi-source fusion and monotonicity-constrained particle filtering
He Yu, Hongru Li
Mechanical Systems and Signal Processing (2022) Vol. 170, pp. 108851-108851
Closed Access | Times Cited: 25
An autoregressive model-based degradation trend prognosis considering health indicators with multiscale attention information
Jichao Zhuang, Yudong Cao, Yifei Ding, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107868-107868
Closed Access | Times Cited: 4
An unsupervised and enhanced deep belief network for bearing performance degradation assessment
Fan Xu, Zhou Fang, Ruoli Tang, et al.
Measurement (2020) Vol. 162, pp. 107902-107902
Closed Access | Times Cited: 38
Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions
ZiYang Di, Haidong Shao, Jiawei Xiang
Science China Technological Sciences (2020) Vol. 64, Iss. 3, pp. 481-492
Closed Access | Times Cited: 34
Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records
Hyunwoo Seo, Jongkyung Shin, Ki-Hun Kim, et al.
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 11, pp. 20398-20412
Open Access | Times Cited: 21
A variational transformer for predicting turbopump bearing condition under diverse degradation processes
Yulang Liu, Jinglong Chen, Tiantian Wang, et al.
Reliability Engineering & System Safety (2022) Vol. 232, pp. 109074-109074
Closed Access | Times Cited: 21
Health Indicator Construction Method of Bearings Based on Wasserstein Dual-Domain Adversarial Networks Under Normal Data Only
Jie Li, Yanyang Zi, Yu Wang, et al.
IEEE Transactions on Industrial Electronics (2022) Vol. 69, Iss. 10, pp. 10615-10624
Closed Access | Times Cited: 19
Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing
Mingkuan Shi, Chuancang Ding, Hongbo Que, et al.
Measurement (2022) Vol. 207, pp. 112299-112299
Closed Access | Times Cited: 19
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