An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

G. A. Susto, J. Wan, S. Pampuri, M. Zanon, A. B. Johnston, P. G. O'Hara, Seán McLoone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.
Original languageEnglish
Title of host publicationAutomation Science and Engineering (CASE), 2014 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages806-811
Publication statusPublished - Aug 2014
Event2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014) - Taipei, Taiwan, Province of China
Duration: 18 Aug 201422 Aug 2014

Conference

Conference2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014)
CountryTaiwan, Province of China
CityTaipei
Period18/08/201422/08/2014

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  • Cite this

    Susto, G. A., Wan, J., Pampuri, S., Zanon, M., Johnston, A. B., O'Hara, P. G., & McLoone, S. (2014). An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance. In Automation Science and Engineering (CASE), 2014 IEEE International Conference on (pp. 806-811). Institute of Electrical and Electronics Engineers (IEEE).