Batch Process Monitoring with Attention-Based Two-Dimensional Stacked Graph Convolutional Autoencoder

In batch processes, the efficacy of statistical control is reportedly sensitive to the dynamics and nonlinearity found in the batch data, which can hamper the valid feature extraction for statistical analysis. Normally, a two-way window is applied to aggregate the samples across the batch and time d...

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Bibliographic Details

Published in:	2024 6th International Conference on Industrial Artificial Intelligence (IAI) pp. 1 - 6
Main Authors:	Gao, Xingke, Zhang, Zheng, Gao, Furong, Zhu, Jinlin
Format:	Conference Proceeding
Language:	English
Published:	IEEE 21.08.2024
Subjects:	Batch process monitoring Batch production systems Convolution Fault detection and diagnosis Feature extraction Graph auto-encoder Monitoring Phase measurement Process control Redundancy Size measurement Statistical analysis Training Two-dimensional modeling
Online Access:	Get full text
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