Input Selection Methods for Soft Sensor Design: A Survey

Francesco Curreri, Giacomo Fiumara and Maria Gabriella Xibilia
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Francesco Curreri: Department of Mathematics and Computer Science, University of Palermo, 90123 Palermo, Italy
Giacomo Fiumara: MIFT Department, University of Messina, 98166 Messina, Italy
Maria Gabriella Xibilia: Department of Engineering, University of Messina, 98166 Messina, Italy

Future Internet, 2020, vol. 12, issue 6, 1-24

Abstract: Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this work and classified with their benefits and drawbacks to guide the designer through this step.

Keywords: soft sensor; inferential model; input selection; feature selection; regression; prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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