Enhancing Industrial Valve Diagnostics: Comparison of Two Preprocessing Methods on the Performance of a Stiction Detection Method Using an Artificial Neural Network

Abstract

The detection and mitigation of stiction are crucial for maintaining control system performance. This paper proposes the comparison of two preprocessing methods for detecting stiction in control valves via pattern recognition via an artificial neural network (ANN). This method utilizes process variables (PVs) and controller outputs (OPs) to accurately identify stiction within control loops. The ANN was comprehensively trained using data from a data-driven model after processing them. Validation and testing were conducted with real industrial data from the International Stiction Database (ISDB), ensuring a practical assessment framework. This study evaluated the impact of two preprocessing methods on fault detection accuracy, namely, the D-value and principal component analysis (PCA) methods, where the D-value method achieved a commendable overall accuracy of 76%, with 86% precision in stiction prediction and a 66% success rate in nonstiction scenarios. This signifies that feature reduction leads to a degraded stiction detection. The data-driven model was implemented in SIMULINK, and the ANN was trained in MATLAB with the Pattern Recognition Toolbox. These promising results highlight the method’s reliability in diagnosing stiction in industrial settings. Integrating this technique into existing control systems is expected to enhance maintenance protocols, reduce operational downtime, and improve efficiency. Future research should aim to expand this method’s applicability to a wider range of control systems and operational conditions, further solidifying its industrial value.