Statistical monitoring of control loops performance: an improved historical-data benchmark index
Article first published online: 25 AUG 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Quality and Reliability Engineering International
Special Issue: Business and Industrial Statistics: Developments and Industrial Practices in Quality and Reliability
Volume 26, Issue 8, pages 831–844, December 2010
How to Cite
Rato, T. J. and Reis, M. S. (2010), Statistical monitoring of control loops performance: an improved historical-data benchmark index. Qual. Reliab. Engng. Int., 26: 831–844. doi: 10.1002/qre.1139
- Issue published online: 29 DEC 2010
- Article first published online: 25 AUG 2010
- control performance assessment;
- control performance monitoring;
- historical-data benchmark;
- variance-based benchmarks
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.