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Reordered short-term autocorrelation-driven long-range discriminative convolutional autoencoder for dynamic process monitoring.
- Source :
-
Journal of Process Control . Mar2024, Vol. 135, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Deep neural networks (DNNs) can result in suboptimal monitoring performance due to nonlinearity, dynamics, and local characteristics in modern complex industrial processes. To surmount these limitations, this paper first proposes a novel data construction method to model the short-term autocorrelation and spatial correlations as a three-dimensional matrix and then reorder the elements of it to better encode the local and temporal structures. Subsequently, we design a new structure called Long-range Discriminative Attention (LDA) based on the self-attention mechanism to enlarge the receptive field of the original convolutional neural networks (CNNs) to extract global features. Finally, we propose a monitoring model named Long-range Discriminative Attention Autoencoder (LDCA) based on LDA to extract structural features between long-range and local variables from the constructed matrix. The effectiveness of the method in fault detection is verified by numerical examples and a three-phase flow process. • Model the spatiotemporal correlation into a three-dimensional tensor. • Reorder elements in data tensor to present the meaning of images. • Long-range discriminative attention mechanism for feature extraction. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09591524
- Volume :
- 135
- Database :
- Academic Search Index
- Journal :
- Journal of Process Control
- Publication Type :
- Academic Journal
- Accession number :
- 175569597
- Full Text :
- https://doi.org/10.1016/j.jprocont.2024.103176