Failure mode and effects analysis (FMEA) is an essential tool for mitigating potential failures, particularly during the ramp-up phases of new products. However, its effectiveness is often limited by the reasoning capabilities of the FMEA tools, which are usually tabular structured. Meanwhile, large language models (LLMs) offer novel prospects for advanced natural language processing tasks. However, LLMs face challenges in tasks that require factual knowledge, a gap that retrieval-augmented generation (RAG) approaches aim to fill. RAG retrieves information from a non-parametric data store and uses a language model to generate responses. Building on this concept, we propose to enhance the non-parametric data store with a knowledge graph (KG). By integrating a KG into the RAG framework, we aim to leverage analytical and semantic question-answering capabilities for FMEA data. This paper contributes by presenting set-theoretic standardization and a schema for FMEA data, an algorithm for creating vector embeddings from the FMEA-KG, and a KG-enhanced RAG framework. Our approach is validated through a user experience design study, and we measure the precision and performance of the context retrieval recall.

翻译：失效模式与影响分析（FMEA）是缓解潜在失效的关键工具，尤其在新产品量产爬坡阶段。然而，其有效性常受限于通常采用表格结构的FMEA工具的分析能力。与此同时，大语言模型（LLMs）为高级自然语言处理任务提供了新的前景。但LLMs在需要事实性知识的任务中面临挑战，检索增强生成（RAG）方法旨在填补这一空白。RAG从非参数化数据存储中检索信息，并利用语言模型生成响应。基于此概念，我们提出使用知识图谱（KG）增强非参数化数据存储。通过将KG集成到RAG框架中，我们旨在为FMEA数据利用分析和语义问答能力。本文的贡献包括：提出FMEA数据的集合论标准化与模式、从FMEA-KG创建向量嵌入的算法，以及一个KG增强的RAG框架。我们的方法通过用户体验设计研究得到验证，并测量了上下文检索召回率的精度与性能。