An artificial intelligence approach for improving plant operator maintenance proficiency

David J. Edwards, Gary D. Holt, Barry Robinson

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Construction plant maintenance practice and its plant operators are inextricably linked. This is because, unlike plant operating within the manufacturing sector, construction plant is largely dependent upon operator skill and competence to maintain the item in a safe, fully operational condition. Research has previously successfully modelled machine breakdown, but revealed that the operator's impact upon machine breakdown rates can be considerable. A conceptual model methodology with which to assess the maintenance proficiency of individual plant operators is presented. Specifically, an artificial intelligent classification model is proposed as a means of classifying plant operator maintenance proficiency into one of three bandings. These are good, average and poor. The results of such work will form the basis of new prescriptive guidelines, for incorporation into the new certificate of training achievement (CTA) scheme, available to inexperienced construction plant operators. The paper concludes with an indication of the palpable benefits of such research, to plant owners and the construction industry at large.

Original languageEnglish
Pages (from-to)239-252
Number of pages14
JournalJournal of Quality in Maintenance Engineering
Volume8
Issue number3
DOIs
Publication statusPublished - 2002
Externally publishedYes

Keywords

  • Artificial intelligence
  • Construction industry
  • Maintenance programmes
  • Operators
  • Plant and machinery
  • Safety

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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