基于自编码器和PCA的滚筒烘丝机多块过程监测方法

李善莲; 安佳敏; 刘朝贤; 张二强; 刘振宇; 杨俊杰; 许冰洋; 张雷

doi:10.12187/2023.06.014

December 2023

Article Contents

LI Shanlian, AN Jiamin, LIU Chaoxian, et al. Multi block process monitoring method of drum dryer based on autoencoder and PCA[J]. Journal of Light Industry, 2023, 38(6): 110-117. doi: 10.12187/2023.06.014

Citation: LI Shanlian, AN Jiamin, LIU Chaoxian, et al. Multi block process monitoring method of drum dryer based on autoencoder and PCA[J]. Journal of Light Industry, 2023, 38(6): 110-117. doi: 10.12187/2023.06.014 shu

Multi block process monitoring method of drum dryer based on autoencoder and PCA

1. Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China;
2. Technology Center, China Tobacco Shanxi Industrial Co., Ltd., Xian 710065, China;
3. Technology Center, China Tobacco Jiangxi Industry Co., Ltd., Nanchang 330096, China;
4. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China

Received Date: 2023-03-15
Accepted Date: 2023-08-19

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 PCA 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 PCA 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%. Compared 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.
- drum dryer,
- autoencoder,
- principal component analysis,
- multi-block modeling,
- process monitoring
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沈阳化工大学材料科学与工程学院沈阳 110142

Multi block process monitoring method of drum dryer based on autoencoder and PCA

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