Control Performance Monitoring with Temporal Features and Dissimilarity Analysis for Nonstationary Dynamic Processes

Abstract

Recently, the combination of cointegration analysis (CA) and slow feature analysis (SFA), has been adopted for concurrent monitoring of operation condition and process dynamics for nonstationary dynamic processes subject to time variant conditions. By isolating long-term temporal equilibrium features and specific temporal slow features from steady-state information, the CA-SFA based monitoring scheme can well distinguish between the changes of operation conditions and real faults. Considering that the temporal variation can provide an indication of control performance changes, the CA-SFA algorithm is further exploited based on dissimilarity analysis of temporal distribution to explore its unique efficacy in control performance monitoring (CPM). Two attractive features of the proposed approach are noticed. First, it is compatible with various operation conditions simultaneously including multifarious steady states and dynamic switchings between different working points. Second, a new performance monitoring index is used to monitor the control performance by quantifying the distribution structure of temporal features against the benchmark from both fast and slow dynamics aspects. Case study on a chemical industrial scale multiphase flow experimental rig shows the feasibility of the new CPM method.