A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification. (March 2020)

• Record Type: Journal Article
• Authors: Kamaruddin, B.; Zabiri, H.; Mohd Amiruddin, A.A.A.; Teh, W.K.; Ramasamy, M.; Jeremiah, S.S.
• Is Part Of: Journal of process control. Volume 87(2020)
• Page Start: 1
• Page End: 16
• Publication Date: 2020-03
• Subjects: Control valve -- Stiction -- Detection -- Shape-based stiction Detection -- Non-Invasive -- Deep Learning -- Convolutional Neural Network -- CNN; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.01.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: A simple model-free butterfly shape-based detection (BSD) method for control valve stiction. A relatively higher generalization capability with successful stiction detection rate of 80% on stiction loops and 72.7% on non-stiction loops. The proposed BSD-CNN quantification algorithm has shown reasonable accuracy when compared to other established methods. Abstract: Control valve stiction is a long-standing problem within process industries. In most methods for shape-based stiction detection, they rely heavily on the traditional controller output (OP) and process variable (PV) plot (i.e. PV-OP plot) that tends to produce an "elliptical" shape which is the widely acknowledged pattern indication for the presence of stiction. However, many of the methods suffered from unsatisfactory generalization capability when subjected to different loop dynamics. In this paper, a "butterfly" shape derived from the manipulation of the standard PV and OP data, which is more robust towards different loop dynamics, is developed for stiction detection. This simple model-free butterfly shape-based detection (BSD) method uses Stenman's one parameter stiction model, which results in a distinctive 'butterfly' pattern in the presence of stiction. The proposed BSD is tested on simulated data, as well as 26 benchmark industrial case studies and has shown a relatively higher generalization capability with relatively higher successful detection rate on stiction loops and on non-stiction loops. A simple quantification algorithm based on BSD-convolutional neural network (BSD-CNN) framework is then developed to quantify the stiction severity. Based on the 15 benchmark industrial loops with stiction, the proposed BSD-CNN quantification algorithm has shown reasonable accuracy when compared to other published quantification methods in literature.