DOI: 10.12187/2023.06.014
Multi block process monitoring method of drum dryer based on autoencoder and PCA
LI Shanlian
AN Jiamin
LIU Chaoxian
ZHANG Erqiang
LIU Zhenyu
YANG Junjie
XU Bingyang
ZHANG Lei
Abstract：The process of drum drying silk has complex characteristics of multivariability,strong coupling,and nonlinearity.Traditional Principal Component Analysis(PCA)method lacks strong nonlinear ability,and global modeling method is difficult to achieve accurate fault detection of the process.Therefore,this paper proposed a novel multi-block approach by integrating Autoencoder(AE)for feature extraction and PC A model.Initially,in order to capture local features,the process variables were divided into blocks according to the drying process principle of tobacco leaf.Secondly,autoencoder was used to extract the nonlinear features of each sub-block.Then,the corresponding PC A models are established for each sub-block,respectively.Lastly,the monitoring results of multiple subspaces were fused for decision-making by Bayesian inference.Two actual leaf silk drying cases were used for verification,and the results showed that the alarm rates of this method were as high as 91.67％and 98.21％.Com-pared to traditional PCA and AE-PCA detection methods,this algorithm could accurately reveal and characterize the overall operating status and local feature information of the drying process,improve the accuracy of anomaly detection in the drum leaf silk drying production process and achieve accurate alarm for quality anomalies,to ensure stable production of the drum leaf silk drying process.
Keywords：drum dryerautoencoderprincipal component analysismulti-block modelingprocess monitoring
Publication Date：2023-12-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 110-117 )