Stiction: a review of the latest research work

Friction can be a help and a hindrance. Without friction, we would not be able to walk – just consider the challenge of walking on an ice rink. But in a control valve, stiction (static friction) between the stem and packing can prevent the valve from responding promptly and accurately to control signals. This article presents abstracts of recent scientific research into stiction.

By KCI Editorial

Experimental and modeling investigation on dynamic response of sticky control valves

Control Engineering Practice, July 2024 (¹) Abstract: the dynamic response of control valves directly affects the safe and efficient operation of industrial control loops. Valve stiction is a common and persistent issue that causes oscillations in control loops. The stiction behaviour of valves has received widespread attention from researchers worldwide in the past two decades. The developed valve stiction models have been widely applied in the detection, quantification and compensation research of sticky control valves. However, how to more accurately characterise stiction behaviour still requires efforts. Most data-driven models do not consider the effects of dynamic response on the stiction behaviour.

In this paper, the inconsistency of the representative stiction models is discussed during the unidirectional motion of the valve stem, and potential improvements are revealed. An experimental device for valve stiction has been designed. This device can replicate valve stiction caused by tight packing, and measure valve position and friction through smart positioner and force sensor. The second-order dynamic system for the sticky valve is constructed by the physical model, and the response time of the valve is calculated and verified combined with the experiment. The effects of sampling interval on stiction behaviour are discussed.

On this basis, an improved valve stiction model considering the dynamic response of the valve is proposed, which supplements the situation of stiction during the unidirectional motion of the valve stem. The proposed model can be applied in control systems with fixed sampling intervals that involve sticky valves. It can also be extended to control systems with variable sampling intervals. This work contributes to evaluating the performance of control systems with sticky valves and providing reference value for the detection, quantification and compensation of valve stiction.

Control valve stiction detection using learning vector quantisation neural network

IFAC conference paper, 2024 (²) Abstract: the performance of a process control loop can be limited when nonlinear problems like deadband, hysteresis, backlash, stiction, etc. exist in control valve. Stiction occurs more frequently than the other valve problems and has potential to cause adverse oscillations in the control loop, resulting in poor quality products, excessive use of raw materials and energy and an environmental footprint.

Timely detection of sticky control valves can help control engineers to take appropriate actions (retuning the controller or using stiction compensation methods) to prevent further degradation of the performance of the control loop.

In connection with the aforesaid fact, this work proposes a novel stiction detection method founded on learning vector quantisation neural network (LVQNN). Simulated database is generated and used to train the LVQNN with the training algorithm: LVQ2.1. To further enhance the performance of the method, transfer learning is adopted to retrain the pre-trained LVQNN model by using industrial data. The retrained LVQNN is tested on practical data obtained from a wide variety of industries. Results highlight that the proposed method can outperform the existing methods.

Stiction detection and recurrence analysis for control valves by phase space reconstruction method

Advanced Engineering Informatics, January 2025 (³) Abstract: valve stiction is a common and persistent fault of industrial loops in process control. The loop oscillations caused by valve stiction pose economic and safety risks to the production process. Detecting valve stiction is crucial for control loop performance evaluation, yet current methods suffer from computational complexity, limited generalisability and low accuracy. Over the past two decades, recurrence analysis based on phase space reconstruction has emerged as a powerful tool to deal with complex nonlinear systems, particularly for detecting subtle changes in signals and systems. Despite this, a highly interpretable and accurate method for extracting features from recurrence plots (RPs) for valve stiction detection has not yet been developed.

In this paper, recurrence analysis of loop signals is conducted from the perspective of phase space reconstruction. By combining the stiction formation mechanism, the statistic and distribution feature indexes are defined to characterise the stiction features in RPs. Based on this, a stiction detection method using the feature index (FI) in RPs is proposed. The performance of the RPs-FI detection method is validated in simulation cases and industrial cases. The impact of different recurrence definitions on stiction detection is discussed.

The detection results show that the performance of the RPs-FI is superior to most detection methods. Notably, the proposed method achieves comparable generalisation and accuracy to the latest machine learning-based detection methods without extensive data training and complex parameter tuning. This detection method demonstrates the great potential of recurrence analysis in valve stiction detection and has the reference value for online monitoring and performance evaluation of industrial loops.

Enhancing industrial valve diagnostics: comparison of two preprocessing methods on the performance of a stiction detection method using an artificial neural network

Applied System Innovation, 2024 (⁴) 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 utilises 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.