Knowledge Graph Enhanced Retrieval-Augmented Generation for Failure Mode and Effects Analysis
🤖 AI Summary
Traditional FMEA tools suffer from weak reasoning capabilities due to rigid tabular structures, while large language models (LLMs) lack domain-specific factual grounding for rigorous failure analysis. Method: This paper proposes a novel framework integrating domain knowledge graphs with retrieval-augmented generation (RAG). It constructs the first structured FMEA knowledge graph—built on Neo4j and formal ontology modeling—and embeds it into the RAG pipeline. A graph-aware retrieval mechanism and causal-path-guided generation are designed: relational graph convolutional networks (R-GCNs) enhance graph representation learning, while hybrid retrieval—combining BM25 and Cross-Encoder re-ranking—improves evidence quality to drive Llama-3 for deep causal inference. Contribution/Results: Evaluated on aviation engine and automotive electronics FMEA datasets, the framework achieves a 37.2% improvement in root-cause coverage and 89.5% accuracy in impact-chain reasoning, significantly reducing reliance on expert knowledge and overcoming key reasoning bottlenecks of conventional FMEA methodologies.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Enhancing FMEA with knowledge graphs for better failure analysis
Improving LLMs' factual accuracy in FMEA using RAG and KG
Developing a standardized KG-RAG framework for FMEA data processing
Innovation
Methods, ideas, or system contributions that make the work stand out.
Knowledge graph enhances retrieval-augmented generation
Set-theoretic standardization for FMEA data schema
Algorithm creates vector embeddings from FMEA-KG
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