A Holistic Approach for Quality Oriented Maintenance Planning Supported by Data Mining Methods

Robert Glawar, Zsolt Kemeny, Tanja Nemeth, Kurt Matyas, L. Monostori, Wilfried Sihn

Research output: Contribution to journalConference article

6 Citations (Scopus)

Abstract

Appropriate maintenance measures, which are carried out at the right time are a key factor to secure plant availability, product quality and process efficiency in modern manufacturing systems. Established maintenance strategies oftentimes lack in combining these strongly related aspects. They are not capable to anticipate in a holistic way and therefore lead to unnecessarily high maintenance efforts, wasted resources and the occurrence of quality and availability impairments. In order to realize a holistic and anticipatory approach for maintenance planning, a methodology which consistently compiles and correlates various data via "cause and effect" coherences is depicted. By breaking down the production facilities on component level a basis is set to link condition monitoring data, wear data, quality and production data by using data mining methods. This framework enables the identification of maintenance-critical conditions and the prediction of failure moments and quality deviations.

Original languageEnglish
Pages (from-to)259-264
Number of pages6
JournalProcedia CIRP
Volume57
DOIs
Publication statusPublished - Jan 1 2016
Event49th CIRP Conference on Manufacturing Systems, CIRP-CMS 2016 - Stuttgart, Germany
Duration: May 25 2016May 27 2016

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Keywords

  • Maintenance
  • Manufacturing system
  • Predictive Model
  • Quality,Data Mining

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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