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Input Selection Methods for Soft Sensor Design: A Survey

Author

Listed:
  • 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)

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.

Suggested Citation

  • Francesco Curreri & Giacomo Fiumara & Maria Gabriella Xibilia, 2020. "Input Selection Methods for Soft Sensor Design: A Survey," Future Internet, MDPI, vol. 12(6), pages 1-24, June.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:6:p:97-:d:367401
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    References listed on IDEAS

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