基于静−动态特性协同感知的复杂工业过程运行状态评价

Current process monitoring and operation performance evaluation methods suffer from inadequate capturing of process information as well as severe missed and false alarms. By performing in-depth analysis of methods for concurrent monitoring static-dynamic characteristic of industrial data, this paper proposes a key performance indicators (KPI)-driven slow feature analysis (SFA) algorithm. It integrates KPI information into SFA model in order to concurrently capture static-dynamic characteristic changes of complex industrial processes. The similarity between latent variables and that between first-order differences are computed to evaluate the optimality of static and transitional operations. On this basis, a unified framework for process operation performance assessment is established based on an integrated perception of static-dynamic characteristics. A sparse learning-based non-optimal factor identification method is proposed to effectively highlight root-cause variables that cause unsatisfactory performance. The feasibility and effectiveness of the proposed method are validated based on data collected from a real-world dense medium coal preparation process and the Tennessee Eastman (TE) process.