An elevator failure mode and effects analysis method based on retrieval augmented generation

Purpose To address the limitations of traditional failure mode and effects analysis (FMEA) methods in elevator fault analysis, including heavy reliance on human experience, limited use of large scale heterogeneous text data, static analysis results and insufficient interpretability, this study aims to develop an intelligent FMEA method for the elevator domain. Design/methodology/approach An elevator FMEA method based on retrieval augmented generation (RAG) is proposed. An external knowledge base is constructed by integrating a knowledge graph (KG) with a vector database. During retrieval, a multi-route retrieval strategy is adopted to obtain candidate documents. A reranking model named CapsGCN-Rank based on a graph convolutional capsule neural network is designed to perform fine-grained filtering and reranking of candidate documents. The reranked documents are then combined with a large language model to generate structured fault analysis results. Findings Experimental results show that the proposed method outperforms several baseline methods in context precision, context recall, as well as the relevance and correctness of generated answers. The method effectively improves the accuracy and completeness of elevator FMEA. Originality/value The proposed approach introduces structured semantics from the KG, a multi-route retrieval strategy and dynamic routing of capsule neural networks into the RAG framework. It enables fine-grained document reranking and interpretable fault analysis for the elevator domain, providing an effective solution for FMEA in complex industrial scenarios.