Jinfu Wang1, Shunyi Zhao1,*, Fei Liu1, Zhenyi Ma2
CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 531-552, 2022, DOI:10.32604/cmes.2022.019521
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… More >
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