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- W2331657364 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." @default.
- W2331657364 created "2016-06-24" @default.
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- W2331657364 date "2012-01-01" @default.
- W2331657364 modified "2023-09-23" @default.
- W2331657364 title "A Mixed Time-/Condition-based Precognitive Maintenance Framework using Support Vectors" @default.
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- W2331657364 doi "https://doi.org/10.2316/p.2012.785-040" @default.
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