Updating Bayesian network for diagnostic failure analysis of construction equipment

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

1 Citation (Scopus)

Abstract

Construction equipment is an important type of resources of heavy construction contractors. Since equipment breakdowns can cause project delays and significant financial losses, the contractors are eager to know those factors causing equipment failures directly or indirectly, related to equipment design, maintenance, and operations. Although Bayesian network can be used for diagnostic analysis of failure events or making predictive analysis, building a Bayesian network for such purpose can be difficult as the cause-effect relations can be subjective and their conditional probabilities change with a wide variety of causal factors. A hybrid approach is proposed in this paper to update the Bayesian diagnostic network structures and parameters using real life data, the conditional probabilities and cause-effect relationships can be dynamically updated with observed failure records to reflect the real life situations of a complex equipment system. A case study is conducted to show the benefits of the hybrid approach in construction equipment diagnostic analysis.
Original languageEnglish
Title of host publicationISARC 2013 - 30th International Symposium on Automation and Robotics in Construction and Mining, Held in Conjunction with the 23rd World Mining Congress
Pages1550-1559
Number of pages10
Publication statusPublished - 1 Dec 2013
Event30th International Symposium on Automation and Robotics in Construction and Mining, ISARC 2013, Held in Conjunction with the 23rd World Mining Congress - Montreal, QC, Canada
Duration: 11 Aug 201315 Aug 2013

Conference

Conference30th International Symposium on Automation and Robotics in Construction and Mining, ISARC 2013, Held in Conjunction with the 23rd World Mining Congress
CountryCanada
CityMontreal, QC
Period11/08/1315/08/13

Keywords

  • Bayesian network learning
  • Construction equipment maintenance
  • Decision support
  • Failure analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Geotechnical Engineering and Engineering Geology
  • Civil and Structural Engineering

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