Improved Stiction Detection via Hybrid Residual Embedded Inception Module Networks

Stiction in control valves, among other problems, presents a formidable challenge in industrial control loops, often resulting in suboptimal system performance. Given its significant impact, stiction detection has become a crucial aspect of controller performance monitoring. While machine learning-based methods for stiction detection have gained traction, this paper investigates the Effectiveness of Inception Networks and Inception-Residual Networks as potential enhancements to the previously proposed CNN method. The results highlight that these adjustments improve accuracy, from 75.3% to 79.45%, using the same training dataset, Effectively capturing variations overlooked by other methods. The application of real industrial data highlights the improvements offered by the proposed framework.