Multivariate control performance assessment and control system monitoring via hypothesis test on output covariance matrices

Abstract

Control loops widely exist in industrial processes, whose performance directly influences the efficiency, safety, and product quality of production plants. Therefore, control performance assessment (CPA) and control system monitoring (CSM) are critically important for industrial processing. In consideration of multivariate control systems, the covariance matrix of closed-loop outputs plays an important role in both CPA and CSM. Existing methods mainly focus on comparing traces or determinants of the output covariance matrices, which only utilize partial information contained in the matrices. As a result, the assessment and monitoring results may be misleading. In this paper, a multiobjective scheme is proposed for both CPA and CSM of multivariate control systems, which takes the entire covariance matrices into account by conducting a hypothesis test on the equality of the matrices. To fulfill the presupposition of such test, autoregressive moving-average (ARMA) filters are established to remove the autocorrelation contained in the closed-loop output data. The developed scheme can be divided into three aspects: CPA using a minimum variance (MV) benchmark, CPA using a user-specified benchmark, and CSM based on historical data. Case studies show that, compared with the conventional approaches, the proposed method provides more abundant information and achieves better results.