Abstract
Online monitoring of batch processes combining subspace design of latent variables with support vector data description
Highlights
To ensure the safe and reliable operation of a batch process, it is necessary to find faults in time
Aiming at the complex correlation and the non-Gaussian distribution among batch process variables, this paper proposes a batch process monitoring algorithm called principal component analysis (PCA)-Multiple subspaces SVDD (MSSVDD), which combines latent variable subspace design with Support vector data description (SVDD)
A batch process monitoring algorithm based on PCA-MSSVDD is proposed by combining latent variable subspace design with SVDD
Summary
To ensure the safe and reliable operation of a batch process, it is necessary to find faults in time. In view of the complex correlation among process variables, many monitoring algorithms based on variable subspace have been studied in recent years[10,11,12,13,14]. The above algorithm based on latent variable subspace design can reduce the risk of local variation characteristics being inundated; the calculation method of the control limit of the model still has the assumption that the data need to obey a Gaussian distribution. Aiming at the complex correlation and the non-Gaussian distribution among batch process variables, this paper proposes a batch process monitoring algorithm called PCA-MSSVDD, which combines latent variable subspace design with SVDD.
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