4.8 Article

A Survey on Deep Learning for Data-Driven Soft Sensors

Journal

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 9, Pages 5853-5866

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3053128

Keywords

Data-driven modeling; deep learning (DL); industrial big data; neural networks (NNs); soft sensor

Funding

  1. National Key Research and Development Program of China [2018YFC0808600]
  2. National Natural Science Foundation of China [61722310]
  3. Natural Science Foundation of Zhejiang Province [LR18F030001]
  4. Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University [ICT20098]

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The article first demonstrates the necessity and significance of deep learning for soft sensor applications by analyzing the merits of deep learning and the trends of industrial processes. It then summarizes mainstream deep learning models, tricks, and frameworks/toolkits, and discusses the demands and problems occurred in practical applications. Finally, outlook and conclusions are given for future research directions.
Soft sensors are widely constructed in process industry to realize process monitoring, quality prediction, and many other important applications. With the development of hardware and software, industrial processes have embraced new characteristics, which lead to the poor performance of traditional soft sensor modeling methods. Deep learning, as a kind of data-driven approach, shows its great potential in many fields, as well as in soft sensing scenarios. After a period of development, especially in the last five years, many new issues have emerged that need to be investigated. Therefore, in this article, the necessity and significance of deep learning for soft sensor applications are demonstrated first by analyzing the merits of deep learning and the trends of industrial processes. Next, mainstream deep learning models, tricks, and frameworks/toolkits are summarized and discussed to help designers propel the developing progress of soft sensors. Then, existing works are reviewed and analyzed to discuss the demands and problems occurred in practical applications. Finally, outlook and conclusions are given.

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