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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:

Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-Shot Intelligent Fault Diagnosis of Machines
Tianci Zhang, Jinglong Chen, Shuilong He, et al.
IEEE Transactions on Industrial Electronics (2022) Vol. 69, Iss. 10, pp. 10573-10584
Closed Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Multi-sensor data fusion-enabled semi-supervised optimal temperature-guided PCL framework for machinery fault diagnosis
Xingxing Jiang, Xuegang Li, Qian Wang, et al.
Information Fusion (2023) Vol. 101, pp. 102005-102005
Closed Access | Times Cited: 50
MgNet: A fault diagnosis approach for multi-bearing system based on auxiliary bearing and multi-granularity information fusion
Jin Deng, Han Liu, Hairui Fang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 193, pp. 110253-110253
Closed Access | Times Cited: 46
Dynamic normalization supervised contrastive network with multiscale compound attention mechanism for gearbox imbalanced fault diagnosis
Yutong Dong, Hongkai Jiang, Wenxin Jiang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108098-108098
Closed Access | Times Cited: 32
Knowledge Embedded Autoencoder Network for Harmonic Drive Fault Diagnosis Under Few-Shot Industrial Scenarios
Jiaxian Chen, Kairu Wen, Jingyan Xia, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 22915-22925
Closed Access | Times Cited: 14
Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts
Mohammed Majid Abdulrazzaq, Nehad T. A. Ramaha, Alaa Ali Hameed, et al.
Mathematics (2024) Vol. 12, Iss. 5, pp. 758-758
Open Access | Times Cited: 14
A class-aware supervised contrastive learning framework for imbalanced fault diagnosis
Jiyang Zhang, Jianxiao Zou, Zhiheng Su, et al.
Knowledge-Based Systems (2022) Vol. 252, pp. 109437-109437
Closed Access | Times Cited: 42
ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps
Yan‐Fu Li, Huan Wang, Muxia Sun
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109850-109850
Open Access | Times Cited: 34
IDSN: A one-stage interpretable and differentiable STFT domain adaptation network for traction motor of high-speed trains cross-machine diagnosis
Chao He, Hongmei Shi, Jianbo Li
Mechanical Systems and Signal Processing (2023) Vol. 205, pp. 110846-110846
Closed Access | Times Cited: 27
Domain Discrepancy-Guided Contrastive Feature Learning for Few-Shot Industrial Fault Diagnosis Under Variable Working Conditions
Tianci Zhang, Jinglong Chen, Shen Liu, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 19, Iss. 10, pp. 10277-10287
Closed Access | Times Cited: 26
Auto-Embedding Transformer for Interpretable Few-Shot Fault Diagnosis of Rolling Bearings
Gang Wang, Dongdong Liu, Lingli Cui
IEEE Transactions on Reliability (2023) Vol. 73, Iss. 2, pp. 1270-1279
Closed Access | Times Cited: 24
Domain distribution variation learning via adversarial adaption for helicopter transmission system fault diagnosis
Kuangchi Sun, Aijun Yin, Shiao Lu
Mechanical Systems and Signal Processing (2024) Vol. 215, pp. 111419-111419
Closed Access | Times Cited: 10
ISEANet: An interpretable subdomain enhanced adaptive network for unsupervised cross-domain fault diagnosis of rolling bearing
Bin Liu, Changfeng Yan, Yaofeng Liu, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102610-102610
Closed Access | Times Cited: 10
Generative artificial intelligence and data augmentation for prognostic and health management: Taxonomy, progress, and prospects
Shen Liu, Jinglong Chen, Yong Feng, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124511-124511
Closed Access | Times Cited: 8
Differential contrast guidance for aeroengine fault diagnosis with limited data
Wenhui He, Lin Lin, Song Fu, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access | Times Cited: 6
Prior knowledge-augmented unsupervised shapelet learning for unknown abnormal working condition discovery in industrial process
Xiaoxue Wan, Lihui Cen, Xiaofang Chen, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102429-102429
Closed Access | Times Cited: 5
Prior Knowledge-Augmented Meta-Learning for Fine-Grained Fault Diagnosis
Yuhang Zhou, Qiang Zhang, Ting Huang, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 6, pp. 8115-8124
Closed Access | Times Cited: 5
Self-supervised enhanced denoising diffusion for anomaly detection
Shu Li, Jiong Yu, Yi Lu, et al.
Information Sciences (2024) Vol. 669, pp. 120612-120612
Closed Access | Times Cited: 5
Prior knowledge-informed multi-task dynamic learning for few-shot machinery fault diagnosis
Tianci Zhang, Jinglong Chen, Zhi‐Sheng Ye, et al.
Expert Systems with Applications (2025), pp. 126439-126439
Closed Access
Bi-structural spatial–temporal network for few-shot fault diagnosis of rotating machinery
Zixu Chen, Jinchen Ji, Qing Ni, et al.
Mechanical Systems and Signal Processing (2025) Vol. 227, pp. 112378-112378
Closed Access
Unlocking the power of knowledge for few-shot fault diagnosis: A review from a knowledge perspective
Pei Ling Lai, Fan Zhang, Tianrui Li, et al.
Information Sciences (2025), pp. 121996-121996
Closed Access
Multi-stage distribution correction: A promising data augmentation method for few-shot fault diagnosis
Xiao Zhang, Weiguo Huang, Rui Wang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106477-106477
Closed Access | Times Cited: 15
Multi-source information fusion meta-learning network with convolutional block attention module for bearing fault diagnosis under limited dataset
Shanshan Song, Shuqing Zhang, Wei Dong, et al.
Structural Health Monitoring (2023) Vol. 23, Iss. 2, pp. 818-835
Closed Access | Times Cited: 15
An Efficient Federated Learning Framework for Machinery Fault Diagnosis With Improved Model Aggregation and Local Model Training
Jiahao Du, Na Qin, Deqing Huang, et al.
IEEE Transactions on Neural Networks and Learning Systems (2023) Vol. 35, Iss. 7, pp. 10086-10097
Closed Access | Times Cited: 14
Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning
Jichao Zhuang, Minping Jia, Cheng‐Geng Huang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 211, pp. 111186-111186
Closed Access | Times Cited: 4
A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples
Jian Tang, Jiawei Xiao, Wentao Chen, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108503-108503
Closed Access | Times Cited: 4
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