Empowering PHM Applications with Time Series Foundation Models: A Unified Multi-Task Learning Approach

Currently, small, task-specific models dominate the development of Prognostics and Health Management (PHM) applications. However, these isolated models often struggle to address the diverse and fragmented requirements present in real industrial environments. The emergence of time-series foundation models has attracted considerable attention, providing a more flexible and effective approach for PHM applications. This study explores how time-series foundation models can enhance PHM applications. Using a typical aero-engine degradation dataset as the research context, we propose a novel unified multi-task learning approach that leverages pre-trained time-series foundation models. Specifically, we utilize these foundation models as the basis for time-series representation learning to tackle various PHM tasks. To accommodate the diverse requirements of these tasks, we design specialized output heads tailored for multi-task learning objectives. The pre-trained foundation model is then fine-tuned with specific datasets to develop localized task-specific models. We validate our approach through case studies using the C-MAPSS datasets. The experimental results demonstrate the feasibility and effectiveness of foundation models for the development of PHM applications.