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Convolutional Neural Network Based Feature Learning for Large-Scale Quality-Related Process Monitoring
- 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.
- Subjects :
- Computer science
Process (computing)
Regression analysis
computer.software_genre
Convolutional neural network
Plot (graphics)
Fault detection and isolation
Computer Science Applications
Control and Systems Engineering
Feature (machine learning)
Data mining
Electrical and Electronic Engineering
computer
Feature learning
Information Systems
Block (data storage)
Subjects
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