Preparation of comprehensive data from huge data sets for predictive soft sensors

Abstract Soft sensors predict values of process variables that are difficult to measure in real time. Predictive ability of adaptive soft sensors depends on databases. However, there is no way to construct initial databases from huge data sets that are measured in plants and that are data rich, but information poor. Therefore, we propose a method to select comprehensive data from huge data sets to build soft sensors with high predictive ability. A genetic algorithm and the Kennard–Stone algorithm are modified for data selection considering predictive ability of regression models and data distribution. Through the analyses of numerical simulation data and real industrial data, we confirm that initial databases could be appropriately constructed from huge data sets and predictive accuracy of soft sensors subsequently increased.

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