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A Nonstationary Process Monitoring Based on Mutual Information among Process Variables

机译：基于过程变量之间的相互信息的非视野过程监控

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摘要

In practical chemical industrial processes, the feed valve is automatically adjusted in response to the control systems and the production load will be adjusted with market situation and administrative regulations. Therefore, process data display nonstationary statistics in practical operation condition and cannot satisfy the ideal assumptions of traditional multivariate statistical methods that process is assumed operating around one preset steady state. Under normal operating conditions, fluctuations or adjustments will only affect the mean and standard deviation of process variables, but the correlation among process variables should follow its inherent mechanism model, whose feature can be statistically captured within certain range. In this paper, a nonstationary process monitoring based on mutual information among process variables is proposed. The Euclidean distance (ED) of eigenvalues of the mutual information matrix under normal operation conditions is calculated to obtain a statistic. Once a fault occurs, the changes in correlation among process variables will be reflected in the mutual information matrix and corresponding ED will exceed the threshold, by which process monitoring can be implemented. A numerical simulation example and a practical cracking process are applied as case studies. The results show a better performance on monitoring nonstationary process than traditional principle component analysis method.
机译：在实用的化学工业过程中，饲料阀根据控制系统自动调整，并将生产负荷与市场情况和行政法规进行调整。因此，过程数据在实际操作条件下显示不间断的统计，不能满足传统多变量统计方法的理想假设，该方法在一个预设稳态上运行过程。在正常操作条件下，波动或调整只会影响过程变量的平均值和标准偏差，但过程变量之间的相关性应遵循其固有机制模型，其特征可以在某些范围内进行统计捕获。本文提出了一种基于过程变量之间的相互信息的非视野过程监控。计算在正常操作条件下互信息矩阵的特征值的欧几里德距离（ED）以获得统计信息。发生故障后，处理变量之间的相关性的变化将反映在互信息矩阵中，并且相应的ED将超过阈值，可以实现进程监控。应用数值模拟示例和实际开裂过程作为案例研究。结果表明，监测非间断过程的性能比传统的原理分析方法更好。

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