Automatic detection and quantification of stiction in control valves

Abstract

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.

Introduction

A typical chemical plant has hundreds of control loops. Control performance is important to ensure tight product quality and low cost of the product in such plants. The presence of oscillation in a control loop increases the variability of the process variables thus causing inferior quality products, larger rejection rates, increased energy consumption, reduced average throughput and profitability. The only moving part in a control loop is the control valve. Control valves frequently suffer from problems such as stiction, leaks, tight packing, and hysteresis. Bialkowski (1992) reported that about 30% of the loops are oscillatory due to control valve problems. In a recent work Desborough and Miller (2001) reported that control valve problems account for about one-third of the 32% of controllers classified as ‘poor’ or ‘fair’ in an industrial survey (Desborough, Miller, & Nordh, 2000). If the control valve contains nonlinearities, e.g., stiction, backlash, and deadband, the valve output may be oscillatory which in turn can cause oscillations in the process output. Among the many types of nonlinearities in control valves, stiction is the most common and one of the long-standing problems in the process industry. It hinders proper movement of the valve stem and consequently affects control loop performance. Stiction can easily be detected using invasive methods such as the valve travel or bump test. But to apply such invasive methods across an entire plant site is neither feasible nor cost-effective because of their manpower, cost and time intensive nature.

Although many invasive tests/methods have been suggested (Aubrun et al., 1995, Gerry and Ruel, 2001, McMillan, 1995, Ruel, 2000, Sharif and Grosvenor, 1998, Taha et al., 1996, Wallén, 1997) for analysis and performance of control valves, only a few non-invasive studies or methods (Horch, 1999, Rengaswamy et al., 2001, Singhal and Salsbury, 2005, Stenman et al., 2003, Yamashita, 2005) have appeared in the literature. Horch's method is successful mainly in detecting valve stiction in flow control loops. It cannot be applied for loops involving an integrator or those carrying compressible fluids. However, Horch and Isaksson (2001) suggested the ‘camel method’ based on the distribution of the second derivatives of the controlled variable to detect valve stiction in an integrating plant. The methods described in Singhal and Salsbury (2005), Rengaswamy et al. (2001), Yamashita (2005) depend on the qualitative shape of the time trends of the data which is often distorted by the presence of noise and disturbances. Also, in real life the shape of the time trends of data is heavily affected by the process and controller dynamics. Stenman et al. (2003) described a model-based segmentation method to detect stiction in control valves. This method requires the model of the process and some tuning parameters. To obtain the closed loop model of the process from routine operation data is generally non-trivial. Moreover, all these methods can only detect stiction but cannot quantify it. As pointed out rightly in Desborough and Miller (2001), ‘a passive or non-invasive method that can reliably and automatically classify valve performance in closed loop is desperately needed in process industry’, a non-invasive method capable of detecting and quantifying stiction will be useful in the process industry to identify valves that need maintenance or repair.

An effective non-intrusive data-based monitoring method could reduce the cost of control loop performance maintenance by screening and short-listing those loops and/or valves that need maintenance. This paper describes a data-based model free non-invasive method that can automatically detect and quantify stiction present in control valves. The main contributions of this paper are:

• A model free method for detecting and quantifying stiction in control valves from routine operating data has been developed. The method does not require the performance of any additional valve travel test or commonly known bump test of the control loop.

• The novel feature of the method is that it can detect and quantify stiction using controlled variable $(\mathit{pv})$, controller output $(\mathit{op})$ and set point $(\mathit{sp})$ data. It does not require valve positioner $(\mathit{mv})$ data. If $\mathit{mv}$ data is available it is very easy to detect and quantify stiction from the mapping of $\mathit{mv}$ and $\mathit{op}$. But this is not the case, when only $\mathit{pv}$, $\mathit{op}$, and $\mathit{sp}$ data are available because the mapping of $\mathit{pv}$ and $\mathit{op}$ is often confounded by the loop dynamics and disturbances. To the best knowledge of the authors, there are no available methods in the literature that can detect and quantify stiction from only $\mathit{pv}$ and $\mathit{op}$ data.

• Finally, the algorithm has been fully automated.

• The method is useful in short-listing the valves suffering from stiction from hundreds or thousands of control valves used in chemical plants or elsewhere. Thus it contributes to reduce the plant maintenance cost and increases the overall profitability of the plant.

The paper has been organized as follows: a formal definition of the stiction is first given, followed by a description and summary of the method used to detect nonlinearity in control loops that the authors have proposed in a separate paper (Choudhury, Shah, & Thornhill, 2003). The main contributions of this paper are the new results on an automated detection scheme for ‘sticky’ valves and the quantification of the amount of stiction present in the valve. Several industrial case studies of the proposed method are presented to demonstrate the practicality and applicability of the proposed methods.