216 citations · 540 across the 21 of their papers we have counts for
23 papers
Evidential Domain Adaptation for Remaining Useful Life Prediction with Incomplete Degradation
Yubo Hou, Mohamed Ragab, Yucheng Wang +5
Accurate Remaining Useful Life (RUL) prediction without labeled target domain data is a critical challenge, and domain adaptation (DA) has been widely adopted to address it by tran…
Target-specific Adaptation and Consistent Degradation Alignment for Cross-Domain Remaining Useful Life Prediction
Yubo Hou, Mohamed Ragab, Min Wu +3
Accurate prediction of the Remaining Useful Life (RUL) in machinery can significantly diminish maintenance costs, enhance equipment up-time, and mitigate adverse outcomes. Data-dri…
Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
Yucheng Wang, Mohamed Ragab, Yubo Hou +3
Remaining Useful Life (RUL) prediction for turbofan engines plays a vital role in predictive maintenance, ensuring operational safety and efficiency in aviation. Although data-driv…
UniFault: A Fault Diagnosis Foundation Model from Bearing Data
Emadeldeen Eldele, Mohamed Ragab, Xu Qing +5
Machine fault diagnosis (FD) is a critical task for predictive maintenance, enabling early fault detection and preventing unexpected failures. Despite its importance, existing FD m…
Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
Peiliang Gong, Mohamed Ragab, Min Wu +4
Test-time adaptation aims to adapt pre-trained deep neural networks using solely online unlabelled test data during inference. Although TTA has shown promise in visual applications…
EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments
Edward, Mohamed Ragab, Yuecong Xu +4
Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. Howev…