ParInfoGPT: An LLM-based two-stage framework for reliability assessment of rotating machine under partial information

Data-driven approaches on large-amount labelled data have been actively implemented for reliability assessment of rotating machine, i.e., with limited labelled data samples. Recently, the study of large language models (LLMs) has demonstrated outstanding performance in the field of natural language processing. Inspired by the LLM for addressing sequential data, this article investigates the implementation of LLM under partial information for reliability assessment of rotating machine. A novel LLM-based two-stage framework, called ParInfoGPT, is proposed by integrating a self-supervised reconstruction network and a weakly supervised classification network. Additionally, a mutual information (MI)-based informative masking strategy for pre-training and a parallel side-adapter (PSA) for fine-tuning are designed to effectively learn the proposed framework. Experiments are systematically conducted and evaluated on two real-world datasets of rotating machine. The experimental results of the proposed methodology demonstrate its superior performance on fault diagnosis under partial information for reliability assessment.