7 papers
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…
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…
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation
Yucheng Wang, Peiliang Gong, Min Wu +4
Time-Series (TS) data has grown in importance with the rise of Internet of Things devices like sensors, but its labeling remains costly and complex. While Unsupervised Domain Adapt…
Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning
Yucheng Wang, Min Wu, Ruibing Jin +3
Sourced from multiple sensors and organized chronologically, Multivariate Time-Series (MTS) data involves crucial spatial-temporal dependencies. To capture these dependencies, Grap…
Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction
Zhihao Wen, Yuan Fang, Pengcheng Wei +3
Predicting Remaining Useful Life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a c…
Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation
Peiliang Gong, Yucheng Wang, Min Wu +3
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained model from an annotated source domain to an unlabelled target domain without accessing the source data, thereby pre…