Assessing, diagnosing, and benchmarking control loops using the input-output cross autocorrelation diagram (IO-CAD)

Monitoring the control loop performance is crucial for operation efficiency and safety in industrial processes. This study proposes a new methodology for control loop performance assessment based on the Input-Output Cross Autocorrelation Diagram (IOCAD), a technique already established in the literature. In this work, two novel indicators based on a polar representation of IOCAD are introduced, complementing four existing indicators previously developed using a Cartesian formulation. By analyzing the autocorrelation between the process variable (PV) and manipulated variable (MV), these indicators enable performance evaluation using only routine plant data. Compared to traditional approaches such as the Minimum Variance Control (MVC), the IOCAD-based method shows greater robustness to noise and setpoint changes, while also providing diagnostic insights into the root causes of performance degradation, such as tuning issues or changes in process dynamics. A Control Performance Indicator (CPI) was also proposed. Simulations involving various control loops, including an offshore oil production control loop, confirmed the method’s effectiveness and applicability for real-time monitoring in diverse operational scenarios.