Work

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

Year: 2025

Type: article

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.

Source: Electronics

DOI: 10.3390/electronics14071265

Authors: Haoliang Cui, Xiansheng Guo, Liyang Yu

Institutions: University of Electronic Science and Technology of China, Beijing University of Posts and Telecommunications

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896