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:

Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model
Wanmeng Ding, Jimeng Li, Weilin Mao, et al.
Reliability Engineering & System Safety (2022) Vol. 232, pp. 109072-109072
Closed Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit
Qing Ni, Jinchen Ji, Ke Feng, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109753-109753
Closed Access | Times Cited: 85
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
Multi-source information joint transfer diagnosis for rolling bearing with unknown faults via wavelet transform and an improved domain adaptation network
Pengfei Liang, Jiaye Tian, Suiyan Wang, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109788-109788
Closed Access | Times Cited: 29
A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings
Rui Bai, Khandaker Noman, Ke Feng, et al.
Reliability Engineering & System Safety (2023) Vol. 238, pp. 109428-109428
Closed Access | Times Cited: 23
Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings
Shilong Yang, Baoping Tang, Weiying Wang, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109716-109716
Closed Access | Times Cited: 22
Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction
Jing Yang, Xiaomin Wang
Reliability Engineering & System Safety (2024) Vol. 244, pp. 109928-109928
Closed Access | Times Cited: 10
A novel machinery RUL prediction method based on exponential model and cross-domain health indicator considering first-to-end prediction time
Xuewu Pei, Xinyu Li, Liang Gao
Mechanical Systems and Signal Processing (2024) Vol. 209, pp. 111122-111122
Closed Access | Times Cited: 10
A deep reinforcement learning-based intelligent fault diagnosis framework for rolling bearings under imbalanced datasets
Yonghua Li, Yipeng Wang, Xing Zhao, et al.
Control Engineering Practice (2024) Vol. 145, pp. 105845-105845
Closed Access | Times Cited: 9
A novel data augmentation framework for remaining useful life estimation with dense convolutional regression network
Jie Shang, Danyang Xu, Haobo Qiu, et al.
Journal of Manufacturing Systems (2024) Vol. 74, pp. 30-40
Closed Access | Times Cited: 9
Acceleration model considering multi‐stress coupling effect and reliability modeling method based on nonlinear Wiener process
Xiaojian Yi, Zhezhe Wang, Shulin Liu, et al.
Quality and Reliability Engineering International (2024) Vol. 40, Iss. 6, pp. 3055-3078
Closed Access | Times Cited: 9
An intelligent hybrid deep learning model for rolling bearing remaining useful life prediction
Linfeng Deng, Wei Li, Xinhui Yan
Nondestructive Testing And Evaluation (2024), pp. 1-28
Closed Access | Times Cited: 8
Bayesian large-kernel attention network for bearing remaining useful life prediction and uncertainty quantification
Lei Wang, Hongrui Cao, Zhi‐Sheng Ye, et al.
Reliability Engineering & System Safety (2023) Vol. 238, pp. 109421-109421
Closed Access | Times Cited: 20
Multi-scale time series analysis using TT-ConvLSTM technique for bearing remaining useful life prediction
Sajawal Gul Niazi, Tudi Huang, Hongming Zhou, et al.
Mechanical Systems and Signal Processing (2023) Vol. 206, pp. 110888-110888
Closed Access | Times Cited: 20
A bearing remaining life prediction method under variable operating conditions based on cross-transformer fusioning segmented data cleaning
Dongxiao Hou, Jiahui Chen, Rongcai Cheng, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 110021-110021
Closed Access | Times Cited: 6
Dynamic normalized health indicator construction and Bayesian recurrent state estimation for remaining useful life prediction of high-speed bearings in wind turbine drivetrain
Xilin Li, Wei Teng, Yangyang Zhang, et al.
Measurement (2025) Vol. 246, pp. 116725-116725
Closed Access
Multi-stage degradation feature with dynamic feedback mechanism for remaining useful life prediction
Chaoge Wang, Jiechen Sun, Xiangyi Meng, et al.
Nondestructive Testing And Evaluation (2025), pp. 1-34
Closed Access
Rolling bearing remaining useful life prediction using deep learning based on high-quality representation
Chenyang Wang, Wanlu Jiang, Lei Shi, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
An unsupervised subdomain adaptation of cross-domain remaining useful life prediction for sensor-equipped equipments
Jianhai Yan, Zhen He, Shuguang He, et al.
Computers & Industrial Engineering (2025), pp. 110967-110967
Closed Access
RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network
Jimeng Li, Weilin Mao, Bixin Yang, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109854-109854
Closed Access | Times Cited: 13
Research on vehicle speed prediction model based on traffic flow information fusion
Zhiyuan Hu, Rui Yang, Liang Fang, et al.
Energy (2024) Vol. 292, pp. 130416-130416
Closed Access | Times Cited: 4
Causal dilated Convolution-Based residual DenseNet with channel attention for RUL prediction of rolling bearings
Jimeng Li, Wanmeng Ding, Weilin Mao, et al.
Measurement (2024) Vol. 235, pp. 115012-115012
Closed Access | Times Cited: 4
Rolling Bearing Degradation Stage Division and RUL Prediction Based on Recursive Exponential Slow Feature Analysis and Bi-LSTM Model
Xinliang Li, Wan Zhang, Yu Ding, et al.
Reliability Engineering & System Safety (2025), pp. 110923-110923
Closed Access
Prediction Model Optimization of Gas Turbine Remaining Useful Life Based on Transfer Learning and Simultaneous Distillation Pruning Algorithm
Yu Zheng, Liang Chen, Xiangyu Bao, et al.
Reliability Engineering & System Safety (2024) Vol. 253, pp. 110562-110562
Closed Access | Times Cited: 3
Aspect-Based Sentiment Analysis of Twitter Influencers to Predict the Trend of Cryptocurrencies Based on Hybrid Deep Transfer Learning Models
Kia Jahanbin, Mohammad Ali Zare Chahooki
IEEE Access (2023) Vol. 11, pp. 121656-121670
Open Access | Times Cited: 10
Robust prediction of remaining useful lifetime of bearings using deep learning
L. Magadán, Juan C. Granda, Francisco J. Suárez
Engineering Applications of Artificial Intelligence (2023) Vol. 130, pp. 107690-107690
Open Access | Times Cited: 10
Page 1 - Next Page

Scroll to top