Manufacturing systems in the industry 4.0 era are becoming increasingly complex and large-scale, creating growing demand for efficient and reliable failure recovery systems. However, traditional manual failure recovery processes often suffer from cognitive overload, inconsistent recovery decisions, and limited scalability. To address these challenges, this paper proposes a retrieval augmented generation (RAG) enhanced failure recovery framework that integrates large language model (LLM) with hierarchical knowledge graph–based knowledge management. The proposed system organizes machine failure data into a structured knowledge graph and performs semantic retrieval over failure metadata and domain specific knowledge to generate recommended recovery solutions. The framework enables scalable and context-aware troubleshooting for manufacturing failure recovery.

Keywords

Large-language model, Knowledge graph, Retrieval-augmented generation, Failure recovery, Industry 4.0