An intelligent condition-based maintenance platform for rotating machinery

V.T. Tran, Bo-Suk Yang

Research output: Contribution to journalArticleAcademicpeer-review

59 Citations (Scopus)


Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications
Original languageEnglish
Pages (from-to)2977-2988
JournalExpert systems with applications
Issue number3
Publication statusPublished - 2012
Externally publishedYes


  • Condition-based maintenance
  • Diagnostics
  • Prognostics
  • Signal processing
  • Feature extraction
  • Feature selection


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