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Streamlining Asset Maintenance throughout Analysis of its Usage Data

  • Hong-Bae Jun
  • Maurice Ruibal
  • Dimitris Kiritsis
  • Paul Xirouchakis
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 257)

Recently, with the advent of emerging technologies such as radio frequency identification (RFID), various sensors, and wireless telecommunication, we can have the visibility of asset status information over the whole asset lifecycle. It gives us new challenging issues for improving the efficiency of asset operations. One of the most challenging problems is the predictive maintenance that makes a prognosis of the asset status via a remote monitoring, predicts the asset's abnormality, and executes suitable maintenance actions such as repair and replacement. In this study, we will develop a prognostic decision algorithm to take suitable maintenance actions by analyzing the degradation status of an asset. To evaluate the proposed approach, we carry out a case study for a heavy machinery.

Keywords

Structural Part Maintenance Strategy Maintenance Operation Heavy Machinery Predictive Maintenance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© International Federation for Information Processing 2008

Authors and Affiliations

  • Hong-Bae Jun
    • 1
  • Maurice Ruibal
    • 1
  • Dimitris Kiritsis
    • 1
  • Paul Xirouchakis
    • 1
  1. 1.EPFL (STI-IPR-LICP)LausanneSwitzerland

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