Finding trustworthy neighbors: Graph aided federated learning for few-shot industrial fault diagnosis with data heterogeneity
Federated fault diagnosis has drawn increased attention recently, which makes use of
datasets from different clients with data privacy. However, data distribution varies across …
datasets from different clients with data privacy. However, data distribution varies across …
Few-Shot Fault Diagnosis Based on an Attention-Weighted Relation Network
L Xue, A Jiang, X Zheng, Y Qi, L He, Y Wang - Entropy, 2023 - mdpi.com
As energy conversion systems continue to grow in complexity, pneumatic control valves may
exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system …
exhibit unexpected anomalies or trigger system shutdowns, leading to a decrease in system …
Domain Adaptation with Multi-adversarial Learning for Open Set Cross-domain Intelligent Bearing Fault Diagnosis
Z Zhu, G Chen, G Tang - IEEE Transactions on Instrumentation …, 2023 - ieeexplore.ieee.org
Adversarial domain adaptation and transfer learning have been widely applied in the field of
cross-domain fault diagnosis. However, the effectiveness of existing domain adaptation …
cross-domain fault diagnosis. However, the effectiveness of existing domain adaptation …
Few-Shot Mechanical Fault Diagnosis for a High-Voltage Circuit Breaker via a Transformer-Convolutional Neural Network and Metric Meta-learning
High-voltage circuit breakers (HVCBs) are responsible for the vital tasks of control and
protection in power grids. Strengthening research on the latent fault diagnosis of HVCBs is …
protection in power grids. Strengthening research on the latent fault diagnosis of HVCBs is …
An information fusion-based meta transfer learning method for few-shot fault diagnosis under varying operating conditions
C Lin, Y Kong, Q Han, T Wang, M Dong, H Liu… - Mechanical Systems and …, 2024 - Elsevier
In recent years, meta-learning has gained increasing attention in the field of fault diagnosis
due to its advantages of handling small samples and exhibiting fast adaptation across …
due to its advantages of handling small samples and exhibiting fast adaptation across …
Few-shot fault diagnosis of turnout switch machine based on flexible semi-supervised meta-learning network
The safety of train operations hinges on the reliability of the signal system, and the switch
machine stands out as a pivotal component within it. Consequently, fault diagnosis of switch …
machine stands out as a pivotal component within it. Consequently, fault diagnosis of switch …
Unified feature learning network for few-shot fault diagnosis
Y Xu, X Ma, X Wang, J Wang, G Tang, Z Ji - Neurocomputing, 2024 - Elsevier
Few-shot fault diagnosis aims at diagnosing the state of mechanical signals with only a few
training samples. Numerous contemporary approaches incorporate Time-Frequency Images …
training samples. Numerous contemporary approaches incorporate Time-Frequency Images …
Small data challenges for intelligent prognostics and health management: a review
Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …
and reducing maintenance costs, and research on intelligent PHM has made significant …
Intelligent Bearing Anomaly Detection for Industrial Internet of Things Based on Auto-Encoder Wasserstein Generative Adversarial Network
R Liu, D Xiao, D Lin, W Zhang - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Bearing anomaly detection plays a crucial role in modern industries as most rotating
machinery faults are attributed to faulty bearings. However, acquiring fault samples in …
machinery faults are attributed to faulty bearings. However, acquiring fault samples in …
An Interpretable Latent Denoising Diffusion Probabilistic Model for Fault Diagnosis Under Limited Data
T Zhang, J Lin, J Jiao, H Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Despite the remarkable success of end-to-end intelligent diagnosis methods, the shortage of
available training data remains one of the most challenging issues in real industrial …
available training data remains one of the most challenging issues in real industrial …