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A novel process monitoring approach based on variational recurrent autoencoder

  • Published:2019-10 Issue: Volume:129 Page:106515
  • ISSN:0098-1354
  • Container-title:Computers & Chemical Engineering
  • language:en
  • Short-container-title:Computers & Chemical Engineering

Author:

Cheng Feifan,He Q. Peter,Zhao Jinsong

Funder

National Natural Science Foundation of China

National Science Foundation

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference41 articles.

1. Reconstruction-based contribution for process monitoring with kernel principal component analysis;Alcala;Ind. Eng. Chem. Res.,2010

2. Variational autoencoder based anomaly detection using reconstruction probability;An;Spec. Lect. IE,2015

3. Neural Networks in Bioprocessing and Chemical Engineering;Baughman,2014

4. Bayer, J., Osendorfer, C., 2014. Learning stochastic recurrent networks. arXiv:1411.7610.

5. Variational principal components;Bishop,1999

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