自编码
软传感器
计算机科学
非线性系统
过程(计算)
人工智能
机器学习
数据建模
回归
数据挖掘
模式识别(心理学)
人工神经网络
数学
统计
数据库
量子力学
操作系统
物理
作者
Linlin Cui,Le Yao,Zhiqiang Ge,Zhihuan Song
标识
DOI:10.1109/ddcls55054.2022.9858375
摘要
Modern industrial processes with increasing complexity not only contain nonlinear and multi-mode characteristics, but also are commonly the dynamic processes, which brought challenging problems to soft sensor modeling. In order to solve these problems, a dynamic mixture variational autoencoder regression (DMVAER) model is proposed for the nonlinear multi-mode modeling, which is suitable for industrial process quality prediction with multiple complex process characteristics. Furthermore, in order to deal with the problem of semi-supervised data with a large number of unlabeled samples, a semi-supervised dynamic mixture variational autoencoder regression (ssDMVAER) model is proposed, and the corresponding semi-supervised data sequence division method is adopted to make full use of the information in both labeled data and unlabeled data. Finally, in order to verify the feasibility and effectiveness of the proposed methods, the two models are applied to an actual industrial process of methanation furnace. The results show that the proposed methods have superior soft sensing performance than existing methods.
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