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.
|Title of host publication||Automation Science and Engineering (CASE), 2014 IEEE International Conference on|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - Aug 2014|
|Event||2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014) - Taipei, Taiwan, Province of China|
Duration: 18 Aug 2014 → 22 Aug 2014
|Conference||2014 IEEE International Conference on Automation Science and Engineering (IEEE CASE 2014)|
|Country||Taiwan, Province of China|
|Period||18/08/2014 → 22/08/2014|
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).