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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:

An information-induced fault diagnosis framework generalizing from stationary to unknown nonstationary working conditions
Jianing Liu, Hongrui Cao, Yang Luo
Reliability Engineering & System Safety (2023) Vol. 237, pp. 109380-109380
Closed Access | Times Cited: 14

Showing 14 citing articles:

Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study
Chao Zhao, Enrico Zio, Weiming Shen
Reliability Engineering & System Safety (2024) Vol. 245, pp. 109964-109964
Closed Access | Times Cited: 72
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
Source-free domain adaptation framework for fault diagnosis of rotation machinery under data privacy
Qikang Li, Baoping Tang, Lei Deng, et al.
Reliability Engineering & System Safety (2023) Vol. 238, pp. 109468-109468
Closed Access | Times Cited: 19
Gradient-based domain-augmented meta-learning single-domain generalization for fault diagnosis under variable operating conditions
Chuanxia Jian, Heen Chen, Chaobin Zhong, et al.
Structural Health Monitoring (2024) Vol. 23, Iss. 6, pp. 3904-3920
Closed Access | Times Cited: 5
Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions
Hongbo Ma, Jiacheng Wei, Guowei Zhang, et al.
Reliability Engineering & System Safety (2024) Vol. 252, pp. 110439-110439
Closed Access | Times Cited: 5
An adaptive source-free unsupervised domain adaptation method for mechanical fault detection
Jianing Liu, Hongrui Cao, Jaspreet Singh Dhupia, et al.
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112475-112475
Closed Access
A new fault detection method based on an updatable hybrid model for hard-to-detect faults in nonstationary processes
Jie Dong, Daye Li, Zhiyu Cong, et al.
Reliability Engineering & System Safety (2025), pp. 110920-110920
Closed Access
Contrast Learning with Hard Example Mining for Few-shot Fault Diagnosis of Rolling Bearings
Zenghui An, Houliang Wang, Yinglong Yan, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 10, pp. 106121-106121
Closed Access | Times Cited: 2
Fault Impulse Inference and Cyclostationary Approximation: A feature-interpretable intelligent fault detection method for few-shot unsupervised domain adaptation
Qing Zhang, Shaochen Li, Tan Chin-Hon, et al.
Reliability Engineering & System Safety (2024) Vol. 253, pp. 110568-110568
Closed Access | Times Cited: 1
Knowledge and Data Dual-Driven Fault Diagnosis in Industrial Scenarios: A Survey
Yimeng Wang, Jie Shen, Shusen Yang, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 11, pp. 19256-19277
Closed Access
Wind Turbine Main Bearing Fault Detection for New Wind Farms with Missing SCADA Data
Jianing Liu, Bingqing Xv, Hongrui Cao, et al.
Mechanisms and machine science (2024), pp. 605-614
Closed Access
Category knowledge-guided few-shot bearing fault diagnosis
Feng Zhan, Lingkai Hu, Wenkai Huang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 139, pp. 109489-109489
Closed Access
Fault diagnosis of rolling bearings under variable operating conditions based on improved graph neural networks
Guochao Chang, Chang Liu, Bingbing Fan, et al.
Engineering Research Express (2024) Vol. 6, Iss. 4, pp. 045231-045231
Closed Access
Research on Classification and Identification of Crack Faults in Steam Turbine Blades Based on Supervised Contrastive Learning
Qinglei Zhang, Longfei Tang, Jiyun Qin, et al.
Entropy (2024) Vol. 26, Iss. 11, pp. 956-956
Open Access
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