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

机译：具有非平稳动态过程的时间特性和相异性分析的控制性能监控

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摘要

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.
机译：最近，已经采用协整分析（CA）和慢特征分析（SFA）的组合来同时监视受时变条件影响的非平稳动态过程的操作条件和过程动力学。通过从稳态信息中隔离长期的时间平衡特征和特定的时间慢特征，基于CA-SFA的监视方案可以很好地区分运行条件的变化和实际故障。考虑到时间变化可以提供控制性能变化的指示，基于时间分布的不相似性分析进一步开发了CA-SFA算法，以探索其在控制性能监控（CPM）中的独特功效。注意到所提出的方法的两个吸引人的特征。首先，它与各种运行条件同时兼容，包括多种稳态和不同工作点之间的动态切换。其次，使用新的性能监视指标通过从快速和慢速动力学方面对照基准对时间特征的分布结构进行量化来监视控制性能。在化学工业规模的多相流实验装置上进行的案例研究表明了这种新的CPM方法的可行性。

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