Monitoring and Diagnosing Process Control Performance: The Single-Loop Case

Currently, only a few, simple overall measures of process and control performance, such as standard deviations, are monitored. This paper presents a hierarchical method for monitoring and diagnosing the performance of single-loop control systems based primarily on normal operating data, which 1) identifies significant deviations from control objectives, 2) determines the best achievable performance with the current control structure, and 3) identifies steps to improve the current performance. Within the last point, the method can isolate whether poor performance is due to the feedforward loop or the feedback loop. If in the feedback loop, it is sometimes possible to determine whether the cause of poor performance is plant/model mismatch or poor tuning. These results are achieved by analyzing autocorrelations and cross correlations of a time series of control loop variables. The theoretical basis of the method is developed and applied to industrial case studies.