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Convolutional Neural Network Based Feature Learning for Large-Scale Quality-Related Process Monitoring

Authors :
Bing Song
Hongbo Shi
Yang Tao
Jiazhen Zhu
Shuai Tan
Source :
IEEE Transactions on Industrial Informatics. 18:4555-4565
Publication Year :
2022
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2022.

Abstract

As industrial technology develops, industrial processes become increasingly large and complex, the traditional methods are difficult to extract features that can represent the condition of the whole process and the effect of fault on quality indicators. Therefore, a novel multi-block decouple convolutional neural network (multi-block DCN) algorithm is proposed. First, key process variables are selected, and process variables are grouped into multiple blocks for the following monitoring. Then, in each block, the proposed DCN constructs a regression model between key process variables and quality indicators, in which the regression model utilizes an improved convolutional neural network as feature extractor and a decoupling layer as feature regularizer. Afterward, the monitoring results of each block are integrated into a global monitoring index based on Bayesian theory. After fault detection, variable oblivion contribution plot is presented to locate faulty variables. Finally, an industrial case is used to demonstrate the effectiveness of multi-block DCN.

Details

ISSN :
19410050 and 15513203
Volume :
18
Database :
OpenAIRE
Journal :
IEEE Transactions on Industrial Informatics
Accession number :
edsair.doi...........170fe39f09b5dfb05f568f42853dcef8
Full Text :
https://doi.org/10.1109/tii.2021.3124578