An adaptive machine learning decision system for flexible predictive maintenance | IEEE Conference Publication | IEEE Xplore

An adaptive machine learning decision system for flexible predictive maintenance


Abstract:

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs a...Show More

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.
Date of Conference: 18-22 August 2014
Date Added to IEEE Xplore: 30 October 2014
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Conference Location: New Taipei, Taiwan

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