A Knowledge Graph-based Link Prediction for Interpretable Maintenance Planning in Complex Equipment

Liqiao Xia, Pai Zheng, Shufei Li, Pin Lyu, C. K.M. Lee, Jialiang Zhou, Kaiye Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Maintenance planning is a significant part of predictive maintenance, which involves task planning, resource scheduling, and prevention. Many data points will be collected during the monitoring and maintenance of sophisticated equipment thanks to the large-scale sensor systems installed in contemporary factories. As a result, with the help of collected maintenance data, maintenance plans may be more detailed and timelier. A knowledge graph (KG) has recently been proposed to manage massive and unorganized maintenance data semantically, enhancing data usage. Despite the fact that previous research had utilized KG for maintenance planning, they had only used semantic searching or graph structure-based algorithms and had not included the prediction of new links. To fill this gap, a maintenance-oriented KG is established firstly based on the well-defined ontology schema and accumulated maintenance data. Then, an Attention-Based Compressed Relational Graph Convolutional Network is proposed to find the potential solutions and explain the fault, specifically for the heterogeneous and sparse graph structure of maintenance-orient KG. A maintenance case of oil drilling equipment is carried out, which compares the proposed model with other cutting-edge models to demonstrate its effectiveness in link prediction.

Original languageEnglish
Title of host publication13th International Conference on Reliability, Maintainability, and Safety
Subtitle of host publicationReliability and Safety of Intelligent Systems, ICRMS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages301-305
Number of pages5
ISBN (Electronic)9781665486903
DOIs
Publication statusPublished - Aug 2022
Event13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022 - Hong Kong, China
Duration: 21 Aug 202224 Aug 2022

Publication series

Name13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022

Conference

Conference13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022
Country/TerritoryChina
CityHong Kong
Period21/08/2224/08/22

Keywords

  • Graph neural network
  • Knowledge graph
  • Maintenance management
  • Maintenance planning
  • Predictive maintenance

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

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