基于知识图谱的商用飞机维修方案推荐系统集成建模

Integrated modeling of commercial aircraft maintenance plan recommendation system based on knowledge graph

针对我国商用飞机智能维修和数字化诊断的需求,面向非结构化故障隔离手册,提出新型BM长短期记忆网络(BM LSTM)算法.运用多数投票法融合条件随机场(CRF)、双向长短期记忆网络(BiLSTM)、BiLSTM CRF 3种实体识别算法,有效提高实体识别精度.基于商用飞机维修故障诊断手册构建维修方案知识图谱,结合词频-逆向文件频率(TF-IDF)相似度算法与BM LSTM算法,设计商用飞机维修方案推荐系统,实现通过检索非结构化故障描述文本准确匹配到维修方案的功能.实验结果表明,利用商用飞机故障隔离手册构建知识图谱、基于所提创新方法开发的维修方案推荐系统,能够有效保证维修信息精确匹配,显著提高维修方案形成效率.

Aiming at the requirements of intelligent maintenance and digital diagnosis of commercial aircraft in China,a novel Boyer-Moore long short-term memory network(BM LSTM)algorithm was proposed for unstructured fault isolation manual.A majority voting method was used to fuse three entity recognition algorithms including conditional random fields(CRF),bi-directional long short-term memory(BiLSTM)and BiLSTM CRF.The accuracy of entity recognition was effectively improved by the proposed BM LSTM algorithm.On this basis,a maintenance scheme knowledge graph was constructed for the commercial aircraft maintenance fault diagnosis manual.A commercial aircraft maintenance scheme recommendation system was designed by combining term frequency-inverse document frequency(TF-IDF)similarity algorithm with BM LSTM.Maintenance schemes can be matched accurately with this recommendation system by retrieving the unstructured fault description texts.Experimental results show that the proposed knowledge graph and the maintenance scheme recommendation system can effectively ensure the accurate matching of maintenance information,and the efficiency of maintenance scheme formation is significantly improved.