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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:

Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions
Haoxuan Zhou, Zihao Lei, Enrico Zio, et al.
Mechanical Systems and Signal Processing (2023) Vol. 191, pp. 110139-110139
Closed Access | Times Cited: 21

Showing 21 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
Domain adaptation with domain specific information and feature disentanglement for bearing fault diagnosis
Shaozhang Xie, Peng Xia, Hanqi Zhang
Measurement Science and Technology (2024) Vol. 35, Iss. 5, pp. 056101-056101
Open Access | Times Cited: 5
SGAD-GAN: Simultaneous Generation and Anomaly Detection for time-series sensor data with Generative Adversarial Networks
Penghui Zhao, Zhongjun Ding, Yang Li, et al.
Mechanical Systems and Signal Processing (2024) Vol. 210, pp. 111141-111141
Closed Access | Times Cited: 5
Physics-informed deep learning framework for explainable remaining useful life prediction
Minjae Kim, Sihyun Yoo, Seho Son, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 143, pp. 110072-110072
Closed Access
Benchmark transformation neural network for health indicator construction under time-varying speed and its application in machinery prognostics
Jiahong Yang, Jianghong Zhou, Yi Chai, et al.
Reliability Engineering & System Safety (2025) Vol. 257, pp. 110823-110823
Closed Access
Statistical distribution measures based on amplitude normalization for wind turbine generator bearing condition monitoring under variable speed conditions
Guangyao Zhang, Yi Wang, Yi Qin, et al.
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112464-112464
Closed Access
Leveraging working-condition-related features for enhanced cross-domain remaining useful life prediction of aircraft engines
Zhiyao Zhang, Jia Cheng, Pengpeng Chen, et al.
The Journal of Supercomputing (2025) Vol. 81, Iss. 4
Closed Access
Anomaly detection of machinery under time-varying operating conditions based on state-space and neural network modeling
Zimin Liu, Zihao Lei, Guangrui Wen, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103285-103285
Closed Access
Hybrid system response model for condition monitoring of bearings under time-varying operating conditions
Haoxuan Zhou, Bingsen Wang, Enrico Zio, et al.
Reliability Engineering & System Safety (2023) Vol. 239, pp. 109528-109528
Closed Access | Times Cited: 11
Construction of bearing health indicator under time-varying operating conditions based on Isolation Forest
Jinwoo Sim, Jin Hong Min, Seokgoo Kim, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107058-107058
Closed Access | Times Cited: 11
Mutual information-based feature disentangled network for anomaly detection under variable working conditions
Chenye Hu, Jingyao Wu, Chuang Sun, et al.
Mechanical Systems and Signal Processing (2023) Vol. 204, pp. 110804-110804
Closed Access | Times Cited: 10
M-band wavelet network for machine anomaly detection from a frequency perspective
Zuogang Shang, Zhibin Zhao, Ruqiang Yan, et al.
Mechanical Systems and Signal Processing (2024) Vol. 216, pp. 111489-111489
Closed Access | Times Cited: 2
Correlation warping radius tracking for condition monitoring of rolling bearings under varying operating conditions
Xiaomeng Li, Yi Wang, Guangyao Zhang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 208, pp. 110943-110943
Closed Access | Times Cited: 5
Unified Flowing Normality Learning for Mechanical Anomaly Detection in Continuous Time-Varying Conditions
Chenye Hu, Jingyao Wu, Chuang Sun, et al.
(2024)
Closed Access | Times Cited: 1
Blade fouling fault detection based on shaft orbit generative adversarial network
Xin Huang, Jun Ma, Huajin Shao, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086119-086119
Closed Access | Times Cited: 1
Novel adaptive approach for anomaly detection in nonlinear and time-varying industrial systems
Álvaro Michelena, Francisco Zayas‐Gato, Esteban Jove, et al.
Logic Journal of IGPL (2024)
Open Access | Times Cited: 1
Unsupervised Anomaly Detection of Machines Operating under Time-varying Conditions: DCD-VAE enabled Feature Disentanglement of Operating Conditions and States
Haoxuan Zhou, Bingsen Wang, Enrico Zio, et al.
Reliability Engineering & System Safety (2024), pp. 110653-110653
Closed Access | Times Cited: 1
Slice-Oriented Signal Probability Distribution Measure for Wind Turbine Generator Bearing Condition Monitoring Under Variable Speed Conditions
Guangyao Zhang, Yi Wang, Liang Guo, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 20, Iss. 4, pp. 5297-5307
Closed Access | Times Cited: 3
Ensembled Multi-classification Generative Adversarial Network for Condition Monitoring in Streaming Data with Emerging New Classes
Yu Wang, Qingbo Wang, Alexei Vinogradov
Structural integrity (2024), pp. 45-57
Closed Access
Unsupervised anomaly detection for manufacturing product images by significant feature space distance measurement
Haoyuan Shen, Baolei Wei, Yizhong Ma
Mechanical Systems and Signal Processing (2024) Vol. 212, pp. 111328-111328
Closed Access
A Simple Yet Effective Data Augmentation for Self-Supervised Multi-stage Centrifugal Pump (MCP) Anomaly Detection
Jiapeng Wu, Diego Cabrera, Mariela Cerrada, et al.
(2023), pp. 1-6
Closed Access
Fleet-Based Transfer Learning for Anomaly Detection in Industrial Systems
Bingsen Wang, Piero Baraldi, Enrico Zio, et al.
(2023)
Closed Access
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