Multi-objective monitoring of closed-loop controlled systems using adaptive LASSO

Abstract

Statistical process control detects nonrandom deviations from a normal distribution. However, most industrial processes are equipped with feedback control loops to reject such disturbances. Hence it is the failure or performance degradation that we should detect instead of mere sensor data patterns that show deviations from normal. In this paper, a multi-objective monitoring approach is proposed to monitor both the system stability and performance. First, closed-loop output data are fitted with an autoregressive moving average model with exogenous inputs (ARMA(X)), using adaptive least absolute shrinkage and selection operator (LASSO). The system stability can be monitored using the largest absolute value of the roots of AR terms. The performance of feedback controller can be monitored based on the Minimum-Variance (MV) principles. The proposed approach was applied to several simulation examples to show that the above two monitoring objects are only sensitive to the faults in processes, but not to disturbances that can be handled by the controller. The ability of the method to localized control loop failure was demonstrated using the Tennessee–Eastman benchmark problem. In addition, we found that the method was able to identify the failure of the outer loop while the inner loop is still performing in a cascade control.