Condition-based maintenance with multi-target classification models

Mark Last, Alla Sinaiski, Halasya Siva Subramania

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Condition-based maintenance (CBM) recommends maintenance actions based on the information collected through condition monitoring. In many modern cars, the condition of each subsystem can be monitored by onboard vehicle telematics systems. Prognostics is an important aspect in a CBM program as it deals with prediction of future faults. In this paper, we present a data mining approach to prognosis of vehicle failures. A multitarget probability estimation algorithm (M-IFN) is applied to an integrated database of sensor measurements and warranty claims with the purpose of predicting the probability and the timing of a failure in a given subsystem. The results of the multi-target algorithm are shown to be superior to a singletarget probability estimation algorithm (IFN) and reliability modeling based on Weibull analysis.

Original languageEnglish
Pages (from-to)245-260
Number of pages16
JournalNew Generation Computing
Issue number3
StatePublished - 1 Jul 2011


  • Condition-based maintenance
  • Info- fuzzy networks
  • Multi-target classification
  • Prognostics
  • Reliability
  • Telematics
  • Vehicle health management

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications


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