文献类型：期刊文献

英文题名：A Soft Sensor for Multirate Quality Variables Based on MC-CNN

作者：Song, Bing[1];Zhou, Yichen[1];Shi, Hongbo[1];Tao, Yang[1];Tan, Shuai[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Manufacturingin Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：36

期号：8

起止页码：13927

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20240915652406);WOS:【SCI-EXPANDED(收录号:WOS:001178998200001)】；

基金：No Statement Available

语种：英文

外文关键词：Feature extraction; Soft sensors; Predictive models; MIMO communication; Correlation; Convolutional neural networks; Training; Convolutional neural network (CNN); deep learning (DL); multirate sampling; quality variables; soft sensor

摘要：In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. In industrial processes, the sampling rates of quality variables are always lower than those of process variables. Meanwhile, the sampling rates among quality variables are also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve this problem, a deep-learning (DL) model based on a multitemporal channels convolutional neural network (MC-CNN) is proposed. In the MC-CNN, the network consists of two parts: the shared network used to extract the temporal feature and the parallel prediction network used to predict each quality variable. The modified BP algorithm makes the blank values generated at unsampled moments not participate in the backpropagation (BP) process during training. By predicting multiple quality variables of two industrial cases, the effectiveness of the proposed method is verified.