Leveraging Pre-Trained GPT Models for Equipment Remaining Useful Life Prognostics

Abstract

Remaining Useful Life (RUL) prediction is crucial for optimizing predictive maintenance and resource management in industrial machinery. However, existing methods struggle with rigid spatiotemporal feature fusion, difficulty in capturing long-term dependencies, and poor performance on small datasets. To address these challenges, we propose a GPT-based RUL prediction model that enhances feature integration flexibility while leveraging few-shot learning and cross-modal knowledge transfer for improved accuracy in both data-rich and data-limited scenarios. Experiments on the NASA N-CMAPSS dataset show that our model outperforms state-of-the-art methods across multiple metrics, enabling more precise maintenance, cost optimization, and sustainable operations.