Control valve stiction detection by use of AlexNet and transfer learning

Abstract

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.