@Article{cmes.2022.019521,
AUTHOR = {Jinfu Wang, Shunyi Zhao, Fei Liu, Zhenyi Ma},
TITLE = {Enhancing the Effectiveness of Trimethylchlorosilane Purification Process Monitoring with Variational Autoencoder},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {132},
YEAR = {2022},
NUMBER = {2},
PAGES = {531--552},
URL = {http://www.techscience.com/CMES/v132n2/48308},
ISSN = {1526-1506},
ABSTRACT = {In modern industry, process monitoring plays a significant role in improving the quality of process conduct. With
the higher dimensional of the industrial data, the monitoring methods based on the latent variables have been
widely applied in order to decrease the wasting of the industrial database. Nevertheless, these latent variables do
not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,
especially the T2 on them. Variational AutoEncoders (VAE), an unsupervised deep learning algorithm using the
hierarchy study method, has the ability to make the latent variables follow the Gaussian distribution. The partial
least squares (PLS) are used to obtain the information between the dependent variables and independent variables.
In this paper, we will integrate these two methods and make a comparison with other methods. The superiority
of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in
terms of the multivariate control charts.},
DOI = {10.32604/cmes.2022.019521}
}