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Identifying Correlations Between Independent Sets of Maintenance and Manufacturing Quality Data

[+] Author Affiliations
Hany El-Gheriani, Martin Guay

Queen’s University, Kingston, ON, Canada

Guillaume Graton

Ecole Centrale de Marseille, Marseille, France

Jorge Arinez

General Motors R&D Center, Warren, MI

Paper No. MSEC_ICMP2008-72030, pp. 53-63; 11 pages
  • ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing
  • ASME 2008 International Manufacturing Science and Engineering Conference, Volume 2
  • Evanston, Illinois, USA, October 7–10, 2008
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4852-4 | eISBN: 978-0-7918-3836-6
  • Copyright © 2008 by ASME and General Motors


Effective equipment maintenance is essential for a manufacturing plant seeking to produce high quality products. The impact of equipment reliability and quality on throughput have been well established, however, the relationship between maintenance and quality is not always clear or direct. This paper describes a statistical modeling method that makes use of a Kalman filter to identify correlations between independent sets of maintenance and quality data. With such a method, maintenance efforts can be better prioritized to satisfy both production and quality requirements. In addition, this method is used to compare results from the theoretical maintenance-quality model to data from an actual manufacturing system. Results of the analysis indicate the potential for this method to be applied to preventive as well as reactive maintenance decisions since ageing aspects of equipment are also considered in the model.

Copyright © 2008 by ASME and General Motors



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