Statistical monitoring of control loops performance: an improved historical‐data benchmark index

Control systems are key elements of virtually all industrial processes, whose performance directly impacts aspects as important as: product quality and variability, operations safety, process efficiency/costs and environmental impact. In this paper we address the problem of monitoring the performance of such control systems, and in particular a new historical-data benchmark index is proposed (IM), which is able to discern between perturbations in the system's core modules, which are under the supervision of process owners, from those originated at the level of disturbances, usually involving other stakeholders. It is a generalization of the current index (Iv) as it can be shown that it reduces to this index for the particular case where the variability of the disturbances is the same as in the reference or benchmark period and in the monitoring period. The results obtained demonstrate that the proposed historical-data benchmark index is able to maintain the target false alarm rate under situations where the variability of the disturbances increases, a situation where the current index, Iv, fails. When the disturbances variability is maintained, both indices present similar detection capability, as expected. The subsequent identification of the modules in fault was also analysed, and the results show that the proposed methodology is able to identify the general source of the degradation in the controller performance, namely, if it is due to a perturbation within the system's core (and which loop is affected) or at the level of the disturbances (increasing variability of the loads or change in their dynamical behaviour). Copyright © 2010 John Wiley & Sons, Ltd.

[1]  Warren R. DeVries,et al.  Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate time-series analysis , 1978 .

[2]  Douglas C. Montgomery,et al.  Some Statistical Process Control Methods for Autocorrelated Data , 1991 .

[3]  Biao Huang,et al.  Performance Assessment of Control Loops , 1999 .

[4]  K. Åström Introduction to Stochastic Control Theory , 1970 .

[5]  T. Harris Assessment of Control Loop Performance , 1989 .

[6]  S. Joe Qin,et al.  Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark , 2008 .

[7]  Daniel W. Apley,et al.  The dynamic T2 chart for monitoring feedback-controlled processes , 2002 .

[8]  Arnold Neumaier,et al.  Estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.

[9]  George Stephanopoulos,et al.  Chemical Process Control: An Introduction to Theory and Practice , 1983 .

[10]  George E. P. Box,et al.  Statistical process monitoring and feedback adjustment: a discussion , 1992 .

[11]  Bengt Klefsjö,et al.  Statistical process adjustment for quality control , 2003 .

[12]  Daniel W. Apley,et al.  The dynamic T 2 chart for monitoring feedback-controlled processes , 2002 .

[13]  C. T. Seppala,et al.  A review of performance monitoring and assessment techniques for univariate and multivariate control systems , 1999 .

[14]  W. Härdle,et al.  Applied Multivariate Statistical Analysis , 2003 .

[15]  S. Qin,et al.  Projection based MIMO control performance monitoring: I—covariance monitoring in state space , 2003 .

[16]  S. Joe Qin,et al.  Recent developments in multivariable controller performance monitoring , 2007 .

[17]  Barry Lennox,et al.  Analysis of multivariable control performance assessment techniques , 2009 .

[18]  Arnold Neumaier,et al.  Algorithm 808: ARfit—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models , 2001, TOMS.

[19]  Fugee Tsung,et al.  Joint Monitoring of PID-Controlled Processes , 1999 .

[20]  Mohieddine Jelali,et al.  An overview of control performance assessment technology and industrial applications , 2006 .

[21]  George E. P. Box,et al.  Statistical Control: By Monitoring and Feedback Adjustment , 1997 .

[22]  T. J. Harris,et al.  Performance assessment of multivariable feedback controllers , 1996, Autom..

[23]  S. Joe Qin,et al.  Statistical MIMO controller performance monitoring. Part II: Performance diagnosis , 2008 .

[24]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .