Abstract
Recent advances in sensors and other streaming data sources of plant floor automation and information systems open an exciting possibility to predict the risks of faults and breakdowns across a manufacturing plant over much longer time horizons than what is conceivable today. This paper introduces a Manufacturing System-wide Balanced Random Survival Forest (MBRSF), a nonparametric machine learning approach that can fuse complex dynamic dependencies underlying these data streams to provide a long-term prognosis of machine breakdowns. Experimental investigations with a 20 machine automotive manufacturing line suggest that MBRSF reduces prediction errors (Brier scores) by over 90% compared to other methods tested.
Published Version
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