Control Performance Monitoring Research Articles

CONTROLLER PERFORMANCE MONITORING AND DIAGNOSIS. INDUSTRIAL PERSPECTIVE

Controller performance monitoring and diagnosis is beneficial to ensure profitable implementation of control in practice. The goal of this paper is share the experience of the author with respect to useful technology that has been developed in this field. Particular attention is given to the problem of monitoring industrial multivariable controllers. Industrial examples and opportunities for further research are presented.

Time series methods for dynamic analysis of multiple controlled variables

Research results in controller performance monitoring for multivariate systems have mainly focused on the problem of estimating the control invariant component of the closed-loop output covariance (Harris, T.J., Boudreau, F. and MacGregor, J.F. Performance assessment of multivariable feedback controllers. Automatica, 32:1505-1518, 1996; Huang, B., Shah, S.L. and Kwok, K.E. Good, bad or optimal? performance assessment of multivariable processes. Automatica, 33(6):1175-1183, 1997b.). The contributions of this paper lie on the dynamic analysis side of multivariate controller performance monitoring, where no a priori information is available, yet assessment of dynamic interactions between loops is of interest. Using results from the statistics, identification, and econometrics literature, graphical methods for analyzing the dynamic performance of vectors of tracking error variables are presented. The multi-output dynamic analysis problem can be simplified considerably by treating the tracking error trends as a vector process of endogenous stochastic variables and using a vector autoregressive (VAR) structure to model the dynamic relationships. Once such a model has been estimated, a host of post-estimation diagnostics, such as multivariate impulse response analysis, can be used to interpret the dynamic interactions between the tracking error variables. These methods will be discussed in detail and demonstrated on simulated and industrial control system data.

Real-Time Control Performance Monitoring as a Tool for Remote Maintenance Service

Modern computer and information technology tools allow real-time control performance monitoring both in processing plants and from a remote location. This allows the development of new kind of service business. In this paper the principles of control performance monitoring is presented. Also, the issues of the remote monitoring are discussed.

Controller Fault Detection Based on Multivariate Statistical Process Control

The ability to detect controller malfunctions is crucial in the monitoring of chemical process because malfunctioning controllers cause abnormal process operation, excess energy consuming and critical process upset. Especially, this paper is concerned to monitor the controller status in MIMO case. The proposed method is aimed to provide a practical basis on the earlier detection of degraded or malfunctioning controller. In this article, a new approach to controller performance monitoring in an online manner is presented that is based on multivariate statistical process control combined with time series analysis. Simulation case to distillation column is applied to demonstrate the suggested approach.

Power, Control and Computer-Based Performance Monitoring: Repertoires, Resistance and Subjectivities

This paper examines Computer-based Performance Monitoring (CBPM) in two UK financial services organizations. In doing so, it examines and critiques the existing manner in which this area has been theorized by both traditional and critical organization theorists. It then offers an alternative analysis of CBPM in terms of power, control and resistance, which involves the close interrogation of subject positioning within the speech of those who are subject to and manage this technology. By examining subject positions in interpretive repertoires, the paper demonstrates how power, control and resistance are constituted at an individual level and are specifically linked to the use (and abuse) of CBPM technology. It then further considers the nature and origins of the interpretive repertoires in relation to their organizational contexts, describing the differential circulation of disciplinary power in each. CBPM is thus understood as a politically neutral technology of power, which, when mobilized by management and discursively interwoven into practice becomes a potent force within local organizational sites. The central message of this paper is that it is possible to reveal the intertwining of individual and institutional discourses purely by examining technologies, practices and subjectivities in local organizational sites.

Control performance monitoring — a review and assessment

In this paper we present an overview of current status of control performance monitoring using minimum variance principles. Extensions to PID-achievable performance assessment, trade-off between performance and robustness, and trade-off between deterministic and stochastic performance objectives are discussed. Future directions are pointed out for research and practice with regard to root-cause diagnosis, plant-wide performance assessment, multivariable assessment, adequacy assessment of existing control strategies, performance assessment of model predictive control, and the use of intelligent field devices and artificial intelligence to form a systematic diagnostic methodology. A brief tutorial on performance assessment is given in the appendix with an industrial process example.

Multivariate Statistical Process Control and Process Performance Monitoring

Multivariate Statistical Process Performance Monitoring (MSPPM) provides a diagnostic tool for the monitoring and detection of process malfunctions for continuous and batch manufacturing processes. This paper initially reviews the concept of process performance monitoring through an industrial application to a fluidised bed-reactor and a simulation of a batch methyl methacrylate polymerisation reactor, prior to describing some of the more recent work being carried out. This includes the development of performance monitoring schemes from minimal process data, the use of multi-block techniques for plant-wide monitoring and the development of generic models for the monitoring of multiple products, grades or recipes.

A Method for Detection of Stiction in Control Valves

There has been an increasing interest in automatic control performance monitoring in recent years. The aim of such monitoring tools is to detect deterioration such as increased variability, oscillating behaviour, saturation or off-sets. Easy and efficient methods have been presented in the literature. After having detected a deteriorated control performance, one would like a monitoring tool to give hints of the possible cause of the deterioration. We will in this contribution suggest a method which allows automatic classification of one important reason for a detected control performance deterioration, namely static friction (stiction) in control valves. The suggested approach uses methodology from the fault-detection field and involves a model-based nonlinear observer and statistical hypothesis testing using a likelihood ratio test. Only little process knowledge, as time-delay, measurements of control signal and process output and some usually known valve parameters are needed. The method is evaluated on industrial data.