5 papers
Heterogeneity-Aware Personalized Federated Learning for Industrial Predictive Analytics
Yuhan Hu, Xiaolei Fang
Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data…
A Multi-head Attention Fusion Network for Industrial Prognostics under Discrete Operational Conditions
Yuqi Su, Xiaolei Fang
Complex systems such as aircraft engines, turbines, and industrial machinery often operate under dynamically changing conditions. These varying operating conditions can substantial…
Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes
Yuqi Su, Xiaolei Fang
Industrial prognostics focuses on utilizing degradation signals to forecast and continually update the residual useful life of complex engineering systems. However, existing progno…
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals
Yuqi Su, Xiaolei Fang
Industrial prognostics aims to develop data-driven methods that leverage high-dimensional degradation signals from assets to predict their failure times. The success of these model…
A Federated Data Fusion-Based Prognostic Model for Applications with Multi-Stream Incomplete Signals
Madi Arabi, Xiaolei Fang
Most prognostic methods require a decent amount of data for model training. In reality, however, the amount of historical data owned by a single organization might be small or not…