发表机构
University of Luxembourg（卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出时间序列检索关联的多模态大语言模型框架，在C-MAPSS基准FD001分区实验显示，该方法可提升RUL预测的误差与稳定性，且效果随模型容量变化。
AI 中文摘要
大型语言模型（LLMs）和智能体AI系统正被越来越多地探索用于特定领域的维护与预测任务，这引发了一个问题：它们能否有效支持预测与健康管理（PHM）。在本文中，我们研究通过时间序列检索关联的多模态大语言模型（MLLMs）进行剩余使用寿命（RUL）估计。我们提出了一个框架，其中从训练集中检索历史上相似的退化片段，并与测试轨迹一起转换为视觉比较工件，该工件通过结构化多模态提示由MLLM处理。该方法在C-MAPSS基准的FD001分区上进行了重复实验评估，将基于检索的推理与基于随机参考选择的无检索基线进行比较。结果表明，时间序列检索在所有评估模型中均一致地改进了基于MLLM的RUL预测，产生更低的误差和更稳定的性能。同时，收益的幅度取决于模型容量，表明当底层MLLM能够利用检索到的证据时，检索最为有效。总体而言，该研究表明时间序列RAG是改进多模态预测推理的有前景机制，同时也突出了基于MLLM的RUL估计在实际PHM场景中的当前局限性。
英文摘要
Large language models (LLMs) and agentic AI systems are increasingly being explored for domain-specific maintenance and prognostics tasks, raising the question of whether they can effectively support prognostics and health management (PHM). In this paper, we investigate remaining useful life (RUL) estimation with multimodal large language models (MLLMs) grounded through time-series retrieval. We propose a framework in which historically similar degradation segments are retrieved from the training set and, together with the test trajectory, transformed into a visual comparison artifact that is processed by the MLLM through a structured multimodal prompt. The approach is evaluated on the FD001 partition of the C-MAPSS benchmark under repeated experiments comparing retrieval-based inference against a non-retrieval baseline based on random reference selection. The results show that time-series retrieval consistently improves MLLM-based RUL prediction across the evaluated models, yielding lower error and more stable performance. At the same time, the magnitude of the benefit depends on model capacity, indicating that retrieval is most effective when the underlying MLLM is able to exploit the retrieved evidence. Overall, the study shows that time-series RAG is a promising mechanism for improving multimodal prognostic reasoning, while also highlighting the current limitations of MLLM-based RUL estimation in practical PHM settings.
DOI：10.36001/phme.2026.v9i1.4969
