###### 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. stiction; simulation; ANN; preprocessing; D-value; principal component analysis (PCA); fault detection +
Footnote †: journal:  
0000-0001-4607-3227][PERSON] 0000-0001-4607-3227][PERSON] 0000-00  
Monitoring these industrial processes is imperative for producing quality end products [3; 4]. The majority of process loops are flow control loops in several industries, such as the oil and gas [5; 6], steel [7; 8], chemical [9; 10], iron and pharmaceutical [11; 12], food [13; 14; 15], and pulp and paper [16; 17] industries, to control material flow and seed flow [18; 19] involving the control of steam, water, material, oil, etc. In a control loop, pneumatic control valves usually act as a final control element. In a flow control loop, pneumatic control valves are used to regulate the flow of any fluid with respect to the control input signal. A pneumatic control valve is one of the essential elements in process industries. Therefore, ensuring that a control valve performs impeccably is critical. Pneumatic actuators are extensively employed in a wide range of industrial applications because they are easy to operate, dependable, and efficient at controlling valves and other mechanical devices. The pneumatic actuator comprises three major subparts as follows: a control valve, a pneumatic servomotor, and a positioner. However, abnormalities may strongly influence the operation of the process loop, thus affecting the end product. The faults associated with pneumatic actuators are categorized into four groups [20], as represented in Figure 1. Among these groups, three are based on the source of abnormalities in parts, such as control valve faults, pneumatic servomotor faults, and positioner faults. The fourth group is general faults, which include faults that are not specific to one particular component. Faults are primarily categorized into two types, namely, abrupt and gradual (incipient) faults, based on the time taken for fault development. When the abnormal behavior of the pneumatic actuator is reflected as a sudden change in the normal behavior, it is considered to be an abrupt fault. These faults take less time to show the change in the behavior of the actuator. Other faults whose abnormalities are not visible through continuous monitoring, as the changes are not distinguishable, are referred to as gradual or incipient faults.