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Academic Journal of Science, Engineering and Technology
Vol. 7 No. 1 (2022): January-Febuary
Original Articles

DEEP KOOPMAN NEURAL NETWORKS FOR NONLINEAR PROCESS MONITORING IN STOCHASTIC PRODUCTION SYSTEMS

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  • Song, Yu-Jie ,
  • Ma, Jian-Guo
Song, Yu-Jie
East China University of Science and Technology, Shanghai, China
Ma, Jian-Guo
East China University of Science and Technology, Shanghai, China

Published 2023-09-20

Keywords

  • Stochastic production system,
  • process monitoring,
  • recurrent neural networks,
  • Long Short-Term Memory,
  • quality constraints

How to Cite

Song, Y.-J., & Ma, J.-G. (2023). DEEP KOOPMAN NEURAL NETWORKS FOR NONLINEAR PROCESS MONITORING IN STOCHASTIC PRODUCTION SYSTEMS. Academic Journal of Science, Engineering and Technology, 7(1), 15–29. Retrieved from https://topjournals.org/index.php/AJSET/article/view/592

Copyright (c) 2023 Academic Journal of Science, Engineering and Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Stochastic production systems (SPS) play a pivotal role in industries such as fermentation, pharmaceuticals, and composite material production, where stringent quality constraints are paramount. To ensure product quality in such systems, effective process monitoring is imperative. However, SPS presents significant challenges due to its inherent stochasticity and measurement uncertainties, stemming from sensitivity to exogenous factors and the lack of accurate in-situ measurements. This paper explores the landscape of SPS process monitoring methods, highlighting their limitations and proposing a novel approach leveraging recurrent neural networks (RNNs), particularly Long Short-Term Memory (LSTM) networks

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