Please use this identifier to cite or link to this item: https://doi.org/10.2316/P.2012.785-040
Title: A mixed time-/condition-based precognitive maintenance framework using support vectors
Authors: Pang, C.K. 
Wang, X.
Zhou, J.
Keywords: ARMAX model
Classification
Condition-based maintenance (CBM)
Corrective maintenance
Predictive maintenance
Support Vector Machine (SVM)
Issue Date: 2012
Source: 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
URI: http://scholarbank.nus.edu.sg/handle/10635/68880
ISBN: 9780889869523
DOI: 10.2316/P.2012.785-040
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