Identification of a process with control valve stiction using a fuzzy system: A data-driven approach

Many researchers focus on detecting and modelling the valve stiction because it has undesirable effects on the control loop performance, which consequently results in poor product quality and increased energy consumption. It is difficult to model a process with a sticky valve using the mathematical definition because of its nonlinear properties such as stiction, hysteresis, dead band and dead zone. This work aims to develop and determine the appropriate model of a process with stiction, which can be used in controller design to mitigate the undesirable effect of the stiction. To achieve this goal by mapping the process with valve stiction to a fuzzy system, a dynamic fuzzy model of the plant is derived through an iterative well-developed fuzzy clustering algorithm, which generates suitable antecedent parameters from a set of input–output measurements that are obtained from the control output (OP) and the process output (PV). To determine the consequent parameters, the least square (LS) estimation is applied. The results reveal that the obtained data-driven Takagi–Sugeno-type (TS) fuzzy rule-based model can effectively represent an appropriate model of the process with stiction for different amounts of stiction that are obtained from the simulation and different industrial loops.     

控制回路中迟滞建模及在线检测

控制阀门中非线性的存在,比如迟滞、死区等,限制了控制回路的性能;大约30%的控制回路振荡是由于阀门的问题,而迟滞是过程工业中所发现的最普遍的阀门问题;尽管已有很多对迟滞现象的理解和建模的尝试,但是仍然缺少一种简单、直观且能比较精确反映真实阀门特性的模型;着重研究阀门迟滞的机理并结合大量的事例,提出了迟滞的模型以及基于系统辨识的在线检测方法,仿真与实际工业应用表明了该方法的有效性与准确性。     

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

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.     

Nonlinear control performance assessment in the presence of valve stiction

Control performance assessment or CPA is a useful tool to establish the quality of industrial feedback control loops, but in practice its usefulness is hampered by nonlinearities in the feedback loop. The Harris index is a popular and easily automated metric used to quantify performance of the loop. However, computing the Harris index simply ignoring common process nonlinearities such as valve stiction will lead to an over-estimate of the index, and consequently a false sense of security. In this paper, we propose two complimentary strategies for accurately assessing the quality of the control performance for loops suffering from modest valve stiction thus avoiding the bias inherent in the standard CPA calculations.     

Frequency analysis and compensation of valve stiction in cascade control loops

Valve stiction is often a common problem in control loops and stiction induced oscillation is the main cause of poor performance in control systems. Cascade control is extensively applied in process industry as an effective strategy to restrain disturbances and compensate process nonlinearities. In recent years many studies have been performed on the detection and quantification of valve stiction in single feedback control loops. However, there is a lack in developing a mechanism which can analyze stiction induced oscillation in cascade control loops. This work focuses on the frequency analysis of stiction induced oscillations in cascade control loops and proposes a mechanism of oscillation compensation through outer and inner controller tuning. The effect of oscillation compensation by changing control strategies is also discussed. The theoretical analysis is evaluated through simulation examples and a pilot-scale flow-level cascade control experiment.     

Detection of asymmetric control valve stiction from oscillatory data using an extended Hammerstein system identification method

The study in this paper is motivated by the detection of control valves with asymmetric stiction resulting in oscillations in feedback control loops. The joint characterization of the control valve and the controlled process is formulated as the identification of a class of extended Hammerstein systems. The input nonlinearity is described by a point-slope-based hysteretic model with two possibly asymmetric ascent and descent paths. An iterative identification method is proposed, based on the idea of separating the ascent and descent paths subject to the oscillatory input and output. The structure of the formulated extended Hammerstein system is shown to be identifiable, and the oscillatory signals in feedback control loops are proved to be informative by exploiting the cyclo-stationarity of these oscillatory signals. Numerical, experimental and industrial examples are provided to illustrate the effectiveness of the proposed identification method.     

A simple method for detection of stiction in control valves

A simple method diagnosing oscillations in process control loops is presented. The new method is based on the cross-correlation between control input and process output and it is shown to correctly distinguish between two important reasons for oscillations in control loops in the process industry, namely external oscillating disturbances and static friction (stiction) in control valves.     

Valve stiction estimation using global optimisation

Valve stiction is the most frequent cause of loop oscillations. Thus, detecting and quantifying this valve problem is essential to ensuring plant profitability. In this work, a novel one-stage procedure to estimate stiction parameters is proposed using a two-parameter stiction model. The optimisation problem is computed using a global optimisation algorithm. These two propositions make the stiction computation more efficient and computationally faster than the currently available method. The applicability of the proposed approach is illustrated using a large number of simulated and industrial valves. Moreover, to isolate the impact of each proposition, the novel method is compared with the currently available technique, which is based on a two-stage scheme. The results show that the global optimisation algorithm is more efficient than the direct search and genetic algorithms, as previously proposed by Jelali (2008). The two-stage procedure provides a better estimate of the apparent stiction, whereas the one-stage procedure provides a better slipjump value.     

System identification applied to stiction quantification in industrial control loops: A comparative study

A comparative study of different models and identification techniques applied to the quantification of valve stiction in industrial control loops is presented in this paper, with the objective of taking into account for the presence of external disturbances. A Hammerstein system is used to model the controlled process (linear block) and the sticky valve (nonlinear block): five different candidates for the linear block and two different candidates for the nonlinear block are evaluated and compared. Two of the five linear models include a nonstationary disturbance term that is estimated along with the input-to-output model, and these extended models are meant to cope with situations in which significant nonzero mean disturbances affect the collected data. The comparison of the different models and identification methods is carried out thoroughly in three steps: simulation, application to pilot plant data and application to industrial loops. In the first two cases (simulation and pilot plant) the specific source of fault (stiction with/without external disturbances) is known and hence a validation of each candidate can be carried out more easily. Nonetheless, each fault case considered in the previous two steps has been found in the application to a large number of datasets collected from industrial loops, and hence the merits and limitations of each candidate have been confirmed. As a result of this study, extended models are proved to be effective when large, time varying disturbances affect the system, whereas conventional (stationary) noise models are more effective elsewhere.     

Valve stiction detection through improved pattern recognition using neural networks

A non-invasive method for detecting valves suffering from stiction using multi-layer feed-forward neural networks (NN) is proposed, via a simple class-based diagnosis. The proposed Stiction Detection Network (SDN) uses a transformation of PV (process variable) and OP (controller output) operational data. Verification of the proposed SDN model’s detection accuracy is done through cross-validation with generated samples and benchmarking with various industrial loops. The industrial loop benchmark predictions of the proposed SDN method has a combined accuracy of 78% (75% in predicting stiction, and 81% for non-stiction) in predicting loop condition, matching capabilities of other established methods in accurately predicting realistic industrial loops suffering from stiction, while also being applicable to all types of oscillatory control signals.     

控制阀粘滞特性的频域检测方法研究

控制阀粘滞特性是导致控制回路振荡的主要原因之一,对控制阀粘滞特性进行了详细分析,在此基础上使用离散傅里叶变换分析振荡的偏差信号,提出了一种频域分析、适用范围较广的控制阀粘滞特性检测方法。通过仿真研究,表明了该方法的有效性。     

Automatic detection and quantification of stiction in control valves

Stiction is a common problem in spring-diaphragm type valves, which are widely used in the process industry. Although there have been many attempts to understand and detect stiction in control valves, none of the current methods can simultaneously detect and quantify stiction. Conventional invasive methods such as the valve travel test can easily detect stiction, but are expensive and tedious to apply to hundreds of valves to detect stiction. Thus there is a clear need in the process industry for a non-invasive method that can not only detect but also quantify stiction so that the valves that need repair or maintenance can be identified, isolated and repaired. This work describes a model free method that can detect and quantify stiction that may be present in control valves using routine operating data obtained from the process. No additional excitation or experimentation of the plant is required. Over a dozen industrial case studies have demonstrated the wide applicability and practicality of this method as an useful diagnostic aid in control loop performance monitoring.     

克服阀粘滞特性的控制方法研究

控制阀粘滞特性在工业过程中非常普遍,本文针对控制阀粘滞特性而引起的回路振荡现象,提出了1种改进的PI控制方法,在一定的误差范围内只用比例控制从而消除振荡,而在误差比较大时采用了自适应PI控制来提高系统性能。同时将改进的PI控制方法转换成模糊控制经验,采用模糊控制克服回路中控制阀粘滞特性,同样达到消除误差的目的。最后在Matlab Simulink平台上,通过设计S函数,实现了控制阀粘滞特性模块和改进的PI控制器,使用模糊控制工具箱实现了模糊控制器的设计,在此基础上分析了在扰动存在的情况下2种控制方法的有效性。     

Estimation of valve stiction in control loops using separable least-squares and global search algorithms

This contribution presents a new procedure for quantifying valve stiction in control loops based on global optimisation. Measurements of the controlled variable (PV) and controller output (OP) are used to estimate the parameters of a Hammerstein system, consisting of a connection of a two-parameter stiction model and a linear low-order process model. As the objective function is non-smooth, gradient-free optimisation algorithms, i.e., pattern search (PS) methods or genetic algorithms (GA), are used for fixing the global minimum of the parameters of the stiction model, subordinated with a least-squares estimator for identifying the linear model parameters. Some approaches for selecting the model structure of the linear model part are discussed. Results show that this novel optimisation-based technique recovers accurate and reliable estimates of the stiction model parameters, dead-band plus stick band (S) and slip jump (J), from normal (closed-loop) operating data for self-regulating and integrating processes. The robustness of the proposed approach was proven considering a range of test conditions including different process types, controller settings and measurement noise. Numerous simulation and industrial case studies are described to demonstrate the applicability of the presented techniques for different loops and for different amounts of stiction.     

Compensation of control valve stiction through controller tuning

Despite numerous studies on modeling and detection of static friction (stiction) in control valves, compensation methods for this problem are limited. In this work, a stiction compensation framework is proposed which is based on the oscillation condition introduced in [17]. This condition was used as a tool to predict occurrence and severity of stiction-induced oscillations in control systems. The aim of this paper is to suggest re-tuning guidelines for controllers with regard to the presence of stiction in the control valve, to eliminate or reduce oscillations. A variety of processes and controllers are studied and recommendations are made in order to eliminate the stiction-induced oscillations. For oscillations that cannot be removed, the proposed method will reduce the frequency and magnitude of oscillations. This compensation framework has also been validated using two different pilot-scale experiments with different types of processes and an industrial control system.     

NLPCA as a diagnostic tool for control valve stiction

A significant number of control loops in process plants perform poorly due to control valve stiction. Developing a method to detect valve stiction in the early phase is imperative to avoid major disruptions to the plant operations. Nonlinear principal component analysis (NLPCA), widely known for its capability in unravelling nonlinear correlations in process data, is extended in this paper to diagnose control valve stiction problems. The present work is based on distinguishing the difference between the shapes of the signals caused by stiction and other sources, and utilizes the operating data of controlled variable-controller output (pv–op). The structure of pv–op data used in this work is of sufficiently low dimension such that the NLPCA’s output allows the usage of simple mathematical tests in quantifying the nonlinear behavior of the loop. It is shown that if the underlying structure of pv–op data is linear, the NLPCA output generally approximates to a straight line with a regression coefficient (R²) greater than 0.8, otherwise there is a possibility of presence of nonlinearity or non-Gaussianity. The presence of stiction is then detected via a new and simple NLPCA curvature index, I_NC. Results from simulated and real industrial case studies show that NLPCA is a very promising tool for detecting valve stiction.     

An improved valve stiction simulation model based on ISA standard tests

The Choudhury valve model is a widely adopted data-driven model to study the behaviour of valve stiction. A recent study (Garcia, 2008) revealed that valve stiction simulation based on Choudhury’s simulation model (Choudhury, Thornhill, & Shah, 2005) fails to pass eight out of fifteen Industry Standard Architecture (ISA) standard tests (ISA, 2000, ISA, 2006) for real control valves. In this study, the ISA testing results are further elaborated for this model. It is found that three minor deficiencies lead to the discrepancies between the Choudhury Model outputs and the expected ones when (i) the valve input signal changes the direction of travel, (ii) the initial stem position does not stay on the working curves l₁ and l₂, and (iii) the valve input signal changes in a ramp–pause–ramp manner. To address the above deficiencies, an improved version of the Choudhury Model, termed as XCH Model, is proposed. Assessments along with the ISA standards presented by Garcia (2008) demonstrate the proposed XCH Model passes all the ISA standard tests and thus provides a more realistic simulation of a real industrial valve being able to exhibit stiction behaviour.     

A simple method for detecting valve stiction in oscillating control loops

This paper presents a simple and new method for detecting valve stiction in an oscillating control loop. The method is based on the calculation of areas before and after the peak of an oscillating signal. The proposed method is intuitive, requires very little computational effort, and is easy to implement online. Analytical results are derived to show the theoretical basis of the new method and field results are presented to show its effectiveness on real world control loops.     

An autonomous valve stiction detection system based on data characterization

This paper proposes a valve stiction detection system which selects valve stiction detection algorithms based on characterizations of the data. For this purpose, novel data feature indexes are proposed, which quantify the presence of oscillations, mean-nonstationarity, noise and nonlinearities in a given data sequence. The selection is then performed according to the conditions on the index values in which each method can be applied successfully. Finally, the stiction detection decision is given by combining the detection decisions made by the selected methods. The paper ends demonstrating the effectiveness of the proposed valve stiction detection system with benchmark industrial data.