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Analyzing Maintenance Data Using Data Mining Methods

  • Carol J. Romanowski
  • Rakesh Nagi
Part of the Massive Computing book series (MACO, volume 3)

Abstract

Preventive maintenance activities generate information that can help determine the causes of downtime and assist in setting maintenance schedules or alarm limits. When the amount of generated data becomes large, humans have difficulty understanding relationships between variables. In this paper, we explore the applicability of data mining, a methodology for analyzing multi-dimensional datasets, to the maintenance domain. Using data mining methods, we identify subsystems responsible for low equipment availability; recommend a preventive maintenance schedule; and find sensors and frequency responses giving the most information about fault types. The data mining approach achieves good, easily understandable results within a short training time.

Keywords

Data Mining Preventive Maintenance Data Mining Algorithm Maintenance Policy Data Mining Method 
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

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Carol J. Romanowski
    • 1
  • Rakesh Nagi
    • 1
  1. 1.Department of Industrial EngineeringState University of New York at BuffaloBuffaloUSA

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