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|Title:||A mixed time-/condition-based precognitive maintenance framework using support vectors|
|Authors:||Pang, C.K. |
Condition-based maintenance (CBM)
Support Vector Machine (SVM)
|Citation:||Pang, C.K.,Wang, X.,Zhou, J. (2012). A mixed time-/condition-based precognitive maintenance framework using support vectors. Proceedings of the IASTED International Conference on Engineering and Applied Science, EAS 2012 : 311-318. ScholarBank@NUS Repository. https://doi.org/10.2316/P.2012.785-040|
|Abstract:||Forecasting of machine outages have been actively pursued in the manufacturing industries to ensure that maintenance is carried out only when required. In this paper, we propose a precognitive maintenance framework based on mixed time- and condition-based models to predict both machine degradation stage and wear. The decision-making framework is based on stage classification using Support Vector Machines (SVMs) and time-based AutoRegressive Moving Average with exogenous inputs (ARMAX) models, and the effectiveness of our proposed methodology is verified with mathematical rigour and simulation studies.|
|Source Title:||Proceedings of the IASTED International Conference on Engineering and Applied Science, EAS 2012|
|Appears in Collections:||Staff Publications|
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