An adaptive machine learning decision system for flexible predictive maintenance

Gian Antonio Susto, Jian Wan, Simone Pampuri, Mattia Zanon, Adrian B. Johnston, Paul G. O'Hara, Seán McLoone

Research output: Chapter in Book/Published conference outputConference publication


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 publication2014 IEEE International Conference on Automation Science and Engineering (CASE)
ISBN (Print)9781479952830
Publication statusPublished - 30 Oct 2014


  • Feature Extraction
  • Industrial Modeling
  • Optical Emission Spectroscopy
  • Predictive Maintenance
  • Sparse Principal Component Analysis
  • Regularization Methods
  • Semiconductor Manufacturing


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