Practical applications of data mining in plant monitoring and diagnostics

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Using available expert knowledge in conjunction with a structured process of data mining, characteristics observed in captured condition monitoring data, representing characteristics of plant operation may be understood, explained and quantified. Knowledge and understanding of satisfactory and unsatisfactory plant condition can be gained and made explicit from the analysis of data observations and subsequently used to form the basis of condition assessment and diagnostic rules/models implemented in decision support systems supporting plant maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of in-service distribution circuit breakers and empirical UHF data captured from laboratory experiments simulating partial discharge defects typically found in HV transformers. This discovered knowledge then forms the basis of two separate decision support systems for the condition assessment/defect clasification of these respective plant items.
Original languageEnglish
Number of pages7
Publication statusPublished - 28 Jun 2007
EventIEEE Power Engineering Society General Meeting 2007. - Tampa, United States
Duration: 24 Jun 200728 Jun 2007


ConferenceIEEE Power Engineering Society General Meeting 2007.
Country/TerritoryUnited States


  • data mining
  • plant monitoring
  • plant operation
  • diagnostics
  • circuit breakers


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