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Deep Learning for Soft Sensor Design

  • Salvatore GrazianiEmail author
  • Maria Gabriella Xibilia
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 867)

Abstract

Soft Sensors are mathematical models used to predict the behavior of real systems. They are usefully applied to estimate hard-to-measure quantities in the process industry. Many Soft Sensors are designed by using data-driven approaches and exploiting historical databases. Machine learning is widely used for this aim. Here, the potentialities of deep learning in solving some challenges raising in industrial applications are introduced. More specifically, the paper focuses on three specific aspects: labelled data scarcity, computational complexity reduction, and unsupervised feature exploitation. The state of the art of Soft Sensors based on deep learning is described. Then, the focus is on Soft Sensors based on Deep Belief Networks, as a research field that the authors have been investigating since years. The improvements offered by Deep Belief Networks, over more conventional data-driven approaches, in designing Soft Sensors for real-world applications will be shown. Soft Sensors for specific cases study are described.

Keywords

Soft sensors System identification Deep learning Deep belief networks Semi-supervised learning Industrial applications 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.DIEEIUniversity of CataniaCataniaItaly
  2. 2.Department of EngineeringUniversity of MessinaMessinaItaly

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