<![CDATA[Effective alarm management is essential in industrial environments to ensure operational safety and minimize costly downtime. Traditional rule-based reporting systems often struggle to handle heterogeneous alarm log formats and the complexity of natural language queries, limiting their adaptability in real-world applications. To address these limitations, this study proposes a generative alarm reporting system that integrates Large Language Models (LLMs) with a Retrieval-Augmented Generation (RAG) framework. The system converts natural language queries into structured JSON filters, enabling efficient retrieval of contextual information from historical alarm logs. Three open-source LLMs—CodeLlama-7B, LLaMA 3.1-8B, and Mistral-7B—were locally deployed and evaluated using both quantitative and qualitative methods. Experimental results show that CodeLlama-7B achieved the best overall performance, with an Exact Match Accuracy of 0.80, a Field Match score of 93.8%, and a 0% Parse Failure Rate, outperforming the other models in reliability and structural consistency. Compared to conventional rule-based approaches, the proposed LLM-RAG integration demonstrates improved relevance, interpretability, and responsiveness in alarm reporting. This work represents the first systematic benchmarking of locally deployed open-source LLMs for industrial alarm management, providing a replicable framework and highlighting their potential to advance intelligent, real-time, and domain-specific reporting in the pulp and paper industry and beyond.]]>
