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

CN 41-1437/TS ISSN 2096-1553

李善莲 , 安佳敏 , 刘朝贤 , 张二强 , 刘振宇 , 杨俊杰 , 许冰洋 , 张雷

李善莲, 安佳敏, 刘朝贤, 等. 基于自编码器和PCA的滚筒烘丝机多块过程监测方法[J]. 轻工学报, 2023, 38(6): 110-117. doi: 10.12187/2023.06.014

引用本文: 李善莲, 安佳敏, 刘朝贤, 等. 基于自编码器和PCA的滚筒烘丝机多块过程监测方法[J]. 轻工学报, 2023, 38(6): 110-117. doi: 10.12187/2023.06.014

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

基于自编码器和PCA的滚筒烘丝机多块过程监测方法

1. 中国烟草总公司郑州烟草研究院, 河南郑州 450001;
2. 陕西中烟工业有限责任公司技术中心, 陕西西安 710065;
3. 江西中烟工业有限责任公司技术中心, 江西南昌 330096;
4. 郑州轻工业大学电气信息工程学院, 河南郑州 450001

作者简介: 李善莲(1979-),男,江西省兴国县人,中国烟草总公司郑州烟草研究院高级工程师,主要研究方向为卷烟加工工艺。E-mail:lshlian@163.com;

基金项目: 国家烟草专卖局烟草科研大数据重大专项项目（110202101083（SJ-07））；河南省科技攻关计划项目（222102220122，222102210118）；烟草行业烟草工艺重点实验室引领计划专项项目（202022AWCX04，202022AWHZ01）
中图分类号: TS43

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

CLC number: TS43

摘要: 鉴于滚筒烘丝干燥过程的多变量、强耦合、非线性的复杂特征，而传统主成分分析（PCA）过程监测方法不具有非线性表达能力及全局建模无法精确检测故障的问题，提出自动编码器特征提取和多块主成分分析联合驱动的多块建模方法。首先，依据叶丝干燥工作原理对变量进行分块，以突出过程局部特征；其次，使用自编码器提取每个子块的非线性特征；再次，分别建立相应的PCA监测模型；最后，通过贝叶斯推理对多个子空间的监测结果进行融合决策。以两个实际叶丝干燥案例进行验证，结果表明：该方法的报警率分别高达91.67%和98.21%，相比传统PCA和AE-PCA检测方法，该方法能准确揭示并表征干燥过程的整体运行状态及局部特征信息，提高对滚筒叶丝干燥生产过程的异常检测精度，实现对质量异常情况的准确报警，有利于保证滚筒叶丝干燥过程的稳定生产。
- 滚筒烘丝机 /
- 自编码器 /
- 主成分分析 /
- 多块建模 /
- 过程监测
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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李善莲, 安佳敏, 刘朝贤, 等. 基于自编码器和PCA的滚筒烘丝机多块过程监测方法[J]. 轻工学报, 2023, 38(6): 110-117. doi: 10.12187/2023.06.014

引用本文: 李善莲, 安佳敏, 刘朝贤, 等. 基于自编码器和PCA的滚筒烘丝机多块过程监测方法[J]. 轻工学报, 2023, 38(6): 110-117. doi: 10.12187/2023.06.014

基于自编码器和PCA的滚筒烘丝机多块过程监测方法

作者简介:李善莲(1979-),男,江西省兴国县人,中国烟草总公司郑州烟草研究院高级工程师,主要研究方向为卷烟加工工艺。E-mail:lshlian@163.com

1. 中国烟草总公司郑州烟草研究院, 河南郑州 450001;
2. 陕西中烟工业有限责任公司技术中心, 陕西西安 710065;
3. 江西中烟工业有限责任公司技术中心, 江西南昌 330096;
4. 郑州轻工业大学电气信息工程学院, 河南郑州 450001

收稿日期: 2023-03-15
录用日期: 2023-08-19

基金项目: 国家烟草专卖局烟草科研大数据重大专项项目（110202101083（SJ-07））；河南省科技攻关计划项目（222102220122，222102210118）；烟草行业烟草工艺重点实验室引领计划专项项目（202022AWCX04，202022AWHZ01）

关键词:

摘要: 鉴于滚筒烘丝干燥过程的多变量、强耦合、非线性的复杂特征，而传统主成分分析（PCA）过程监测方法不具有非线性表达能力及全局建模无法精确检测故障的问题，提出自动编码器特征提取和多块主成分分析联合驱动的多块建模方法。首先，依据叶丝干燥工作原理对变量进行分块，以突出过程局部特征；其次，使用自编码器提取每个子块的非线性特征；再次，分别建立相应的PCA监测模型；最后，通过贝叶斯推理对多个子空间的监测结果进行融合决策。以两个实际叶丝干燥案例进行验证，结果表明：该方法的报警率分别高达91.67%和98.21%，相比传统PCA和AE-PCA检测方法，该方法能准确揭示并表征干燥过程的整体运行状态及局部特征信息，提高对滚筒叶丝干燥生产过程的异常检测精度，实现对质量异常情况的准确报警，有利于保证滚筒叶丝干燥过程的稳定生产。

English Abstract

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.

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基于自编码器和PCA的滚筒烘丝机多块过程监测方法

作者简介: 李善莲(1979-),男,江西省兴国县人,中国烟草总公司郑州烟草研究院高级工程师,主要研究方向为卷烟加工工艺。E-mail:lshlian@163.com;

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

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通讯作者: 陈斌, bchen63@163.com

基于自编码器和PCA的滚筒烘丝机多块过程监测方法

作者简介:李善莲(1979-),男,江西省兴国县人,中国烟草总公司郑州烟草研究院高级工程师,主要研究方向为卷烟加工工艺。E-mail:lshlian@163.com

English Abstract

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

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