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Detecting anomalies in attributed networks through sparse canonical correlation analysis combined with random masking and padding
Attributed networks are prevalent in the current information infrastructure, where node
attributes enhance knowledge discovery. Anomaly detection in attributed networks is …
attributes enhance knowledge discovery. Anomaly detection in attributed networks is …
A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples
J Tang, J ** algorithms that can estimate and predict the health …
Self-supervised knowledge mining from unlabeled data for bearing fault diagnosis under limited annotations
D Kong, L Zhao, X Huang, W Huang, J Ding, Y Yao… - Measurement, 2023 - Elsevier
Deep learning has become a popular approach for fault diagnosis due to its powerful feature
extraction and adaptability. However, its reliance on extensive annotations poses …
extraction and adaptability. However, its reliance on extensive annotations poses …
CaCo: Attributed network anomaly detection via canonical correlation analysis
Capturing the complex interaction between the node attribute and the network structure is
important for attributed network embedding and anomaly detection. However, there are few …
important for attributed network embedding and anomaly detection. However, there are few …
Imbalanced source-free adaptation diagnosis for rotating machinery
There have been some studies on fault diagnosis in source-free domain adaptation (SFDA)
environments, but, currently, all studies assume that the fault types are uniform. When fault …
environments, but, currently, all studies assume that the fault types are uniform. When fault …
A multi-scale graph-guided dynamic enhanced alignment network for mechanical fault diagnosis considering domain shift and data imbalance
X Fan, L Duan, N Zhang - Neurocomputing, 2025 - Elsevier
Transfer learning is widely applied in the cross-domain diagnosis of machines, often under
the assumption of balanced data. However, machines mostly operate in the normal state in …
the assumption of balanced data. However, machines mostly operate in the normal state in …
A Data and Knowledge Fusion‐Driven Early Fault Warning Method for Traction Control Systems
While high‐speed maglev trains offer convenient travel options, they also pose challenging
issues for fault detection and early warning in critical components. This study proposes a …
issues for fault detection and early warning in critical components. This study proposes a …
Intelligent Fault Diagnosis of Bearings in Unsupervised Dynamic Domain Adaptation Networks Under Variable Conditions
Q Zhang, Z Lv, C Hao, H Yan, Q Fan - IEEE Access, 2024 - ieeexplore.ieee.org
Effective fault diagnosis is crucial for ensuring the safe and reliable operation of machinery.
Despite satisfactory achievements of deep learning in fault diagnosis, acquiring large …
Despite satisfactory achievements of deep learning in fault diagnosis, acquiring large …