融合深度学习的航空发动机故障诊断知识图谱的构建及应用

打开文本图片集

Abstract：Inresponsetothechalengethat traditionalmethodsofdiagnosingfaultsinaero-engineeasilyleadtoknowledge wastageand gradual inadequacyforcomplexwork demands，knowledge graphscanbeanefectivesolution.Thisarticleproposes amethodforconstructingandapplyingaknowledgegraphforthediagnosisofaircraftenginefaults，integratingdeeplearing.A named entityrecognitionalgorithmisintroducedbasedonBERT-BiLSTM-Atention-CRFforidentifying entitiesinthecorpusof aircraft enginefault diagnosis.BERTis leveraged to generatecharacter embedding features，BiLSTMisutlizedtocapture contextualfeatures，andatentionmechanismsareincorporatedtohighlightthekeyinformationfrombothforwardandbackward contexts.Experimentalresultsinnamedentityrecognitiondemonstratethattheproposedalgorithmcanenhanceentity identificationefctiveness，providing prerequisitefortheknowledgegraph construction.Basedoncorpussyntax，entity relationshipsarematched，andtheNeo4jgraphdatabaseisappliedasthefoundationforhighlyvisualizedmanagementofthe knowledge graphAndthe knowledge graph is harnessd todesign an intellgent question-answering，enhancing fault resolution ratesand providing knowledge support for autonomous fault diagnosis.

Keywords：aero-engine;fault diagnosis;knowledge graph;entity recognition;knowledge Q&A

0 引言

航空发动机作为现代工业中的关键技术和产品，是国家科技、国防和经济发展战略的重要组成部分[]。（剩余6018字）