Computer Science ›› 2023, Vol. 50 ›› Issue (5): 82-92.doi: 10.11896/jsjkx.221100160

• Explainable AI • Previous Articles     Next Articles

Mechanical Equipment Fault Diagnosis Driven by Knowledge

DONG Jiaxiang, ZHAI Jiyu, MA Xin, SHEN Leixian, ZHANG Li   

  1. School of Software,Tsinghua University,Beijing 100084,China
  • Received:2022-11-18 Revised:2023-02-24 Online:2023-05-15 Published:2023-05-06
  • About author:DONG Jiaxiang,born in 1994,doctoral student.His main research interests include construction and application of domain knowledge graph,big data analysis.
    ZHANG Li,born in 1960,Ph.D,professor,Ph.D supervisor.His main research interests include knowledge graph,industrial big data,and real-time computing.
  • Supported by:
    National Natural Science Foundation of China(71690231).

Abstract: With the rapid development of social economy,modern industry now presents a trend featuring complex research objects,informationalized application methods and diversified production modes.Industrial fault diagnosis,as one of the most important research areas in modern industry,is still facing a series of technical bottlenecks due to the complexity of mechanical equipment and the lack of referential knowledge.In order to solve the above problems,this paper proposes a knowledge-driven fault diagnosis scheme for mechanical equipment,which mainly includes two parts——knowledge construction and diagnosis process.In terms of knowledge construction,this paper presents a domain knowledge graph construction method.In terms of diagnosis process,this paper designs a general mechanical equipment fault diagnosis process consisting of four steps,fault inquiry,fault location,fault cause location and fault maintenance guidance.To date,the scheme has been actually applied in a large excavator maintenance provider in China,and its effectiveness has been verified.Experimental results indicate the scheme improves the know-ledge and intelligent level of excavator fault diagnosis and shows high accuracy and practicability.The application of the scheme will be further promoted in the industry.

Key words: Mechanical equipment, Fault diagnosis, Knowledge driven, Domain knowledge graph

CLC Number: 

  • TP311
[1]LI Q Y.Engineering machinery(2nd edition)[M].Changsha:Central South University Press,2012:12-14.
[2]LI J G,ZHANG J,GU Y.Mechanical fault diagnosis[M].Beijing:Chemical Industry Press,1999:23-25.
[3]WANG G B,HE Z J,CHEN X F,et al.Basic research on machinery fault diagnosism what is the prescription[J].Journal of Mechanical Engineering,2013,49(1):63-72.
[4]JI S,PAN S,CAMBRIA E,et al.A survey on knowledgegraphs:Representation,acquisition,and applications[J].IEEE Transactions on Neural Networks and Learning Systems,2021,33(2):494-514.
[5]WANG H F,QI G L,CHEN H J.Knowledge graph:methods,practices and applications[M].Beijing:Electronic Industry Press,2020:56-67.
[6]HOGAN A,BLOMQVIST E,COCHEZ M,et al.Knowledgegraphs[J].ACM Computing Surveys(CSUR),2021,54(4):1-37.
[7]SHADBOLT N,BERNERS-LEE T,HALL W.The semanticweb revisited[J].IEEE Intelligent Systems,2006,21(3):96-101.
[8]LI C,LI A,WANG Y,et al.A survey on approaches and applications of knowledge representation learning[C]//2020 IEEE Fifth International Conference on Data Science in Cyberspace(DSC).New York:IEEE Press,2020:312-319.
[9]LENAT D B,PRAKASH M,SHEPHERD M.CYC:Using com-mon sense knowledge to overcome brittleness and knowledge acquisition bottlenecks[J].AI Magazine,1985,6(4):65-65.
[10]MILLER G A.WordNet:a lexical database for English[J].Communications of the ACM,1995,38(11):39-41.
[11]LIU H,SINGH P.ConceptNet-a practical commonsense rea-soning tool-kit[J].BT Technology Journal,2004,22(4):211-226.
[12]BOLLACKER K,COOK R,TUFTS P.Freebase:a shared database of structured general human knowledge[C]//Proceedings of the 22nd National Conference on Artificial ntelligence.Menlo Park,CA:AAAI,2007:1962-1963.
[13]VRANDEČIĆ D,KRÖTZSCH M.Wikidata:a free collaborative knowledgebase[J].Communications of the ACM,2014,57(10):78-85.
[14]WANG H,DING T Y,BROWN J L,et al.Data Driven Fault Diagnosis and Fault Tolerant Control:Some Advances and Possible New Directions[J].Acta Automatica Sinica,2009,35(6):739-747.
[15]GAO Z,CECATI C,DING S X.A survey of fault diagnosis and fault-tolerant techniques-Part I:Fault diagnosis with model-based and signal-based approaches[J].IEEE Transactions on Industrial Electronics,2015,62(6):3757-3767.
[16]BEARD R V.Failure accomodation in linear systems throughself-reorganization[D].Cambridge:Massachusetts Institute of Technology,1971.
[17]DADON I,KOREN N,KLEIN R,et al.A realistic dynamicmodel for gear fault diagnosis[J].Engineering Failure Analysis,2018,84:77-100.
[18]YANG G,YU S.Construction Research ofDiagnostic Know-ledge Base Based on Decision Technology and RS Theory[C]//Recent Developments in Intelligent Computing,Communication and Devices(ICCD 2017).Singapore:Springer 2019:311-323.
[19]ASKIN R G,DROR M,VAKHARIA A J.Printed circuitboardfamily grouping and component allocation for a multimachine,open-shop assembly cell[J].Naval Research Logistics(NRL),1994,41(5):587-608.
[20]COSTAMAGNA P,DE GIORGI A,MOSER G,et al.Data-dri-ven techniques for fault diagnosis in power generation plants based on solid oxide fuel cells[J].Energy Conversion and Ma-nagement,2019,180:281-291.
[21]NOY N F,MCGUINNESS D L.Ontology development 101:A guide to creating your first ontology[J].Stanford Knowledge Systems Laboratory Technical Report KSL-01-05,2001(5):1-33.
[22]Neo4j.(2021)[EB/OL].http://www.neo4j.com.
[23]GOU J,JIANG Y,WU Y,et al.A New Knowledge FusionMethod Based on Semantic Rules[C]//2006 8th International Conference on Signal Processing.New York:IEEE Press,2006:3.
[24]RISTAD E S,YIANILOS P N.Learning string-edit distance[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(5):522-532.
[25]SADOWSKI C,LEVIN G.Simhash:Hash-based similarity detection[R].Santa Cruz:University of California,2011.
[26]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013.
[27]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
[28]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Advances in Neural Information Processing Systems.Cambridge,MA:MIT Press,2013:2787-2795.
[29]CARD S K,MORAN T P,NEWELLA.The keystroke-levelmodel for user performance time with interactive systems[J].Communications of the ACM,1980,23(7):396-410.
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