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Open AccessReview

Input Selection Methods for Soft Sensor Design: A Survey

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Department of Mathematics and Computer Science, University of Palermo, 90123 Palermo, Italy
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MIFT Department, University of Messina, 98166 Messina, Italy
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Department of Engineering, University of Messina, 98166 Messina, Italy
*
Author to whom correspondence should be addressed.
Future Internet 2020, 12(6), 97; https://doi.org/10.3390/fi12060097
Received: 6 May 2020 / Revised: 1 June 2020 / Accepted: 1 June 2020 / Published: 4 June 2020
(This article belongs to the Collection Featured Reviews of Future Internet Research)
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. View Full-Text
Keywords: soft sensor; inferential model; input selection; feature selection; regression; prediction soft sensor; inferential model; input selection; feature selection; regression; prediction
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Curreri, F.; Fiumara, G.; Xibilia, M.G. Input Selection Methods for Soft Sensor Design: A Survey. Future Internet 2020, 12, 97.

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