| Issue |
E3S Web Conf.
Volume 287, 2021
International Conference on Process Engineering and Advanced Materials 2020 (ICPEAM2020)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 6 | |
| Section | Process Systems Engineering & Optimization | |
| DOI | https://doi.org/10.1051/e3sconf/202128703012 | |
| Published online | 06 July 2021 | |
Control valve stiction detection by use of AlexNet and transfer learning
1 Department of Chemical Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
2 CO2RES, Institute of Contaminant Management, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, Malaysia
3 Western Australian School of Mines, Curtin University, GPO Box U1987, 6845 WA, Australia
* Corresponding author: chris.aldrich@curtin.edu.au
Control valve stiction is a common problem faced by the process industries, which can have a strong adverse effect on the profitable operation of plants. Although various stiction detection methods based on neural networks have been proposed, few of these studies have considered the performance of stiction detection based on the use of 2D representations of the process signals. In this paper, such an approach is proposed, based on the use of a pretrained convolutional neural network, AlexNet. The proposed convolutional neural network stiction detection (CNN-SD) method showed highly satisfactory performance, which can be further applied on real industrial data.
© The Authors, published by EDP Sciences, 2021
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