Chee Khiang Pang, Xiaoyun Wang, and Junhong Zhou
ARMAX model, classification, condition-based Maintenance (CBM), corrective maintenance, predictive maintenance, Support Vector Machine (SVM)
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.
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