Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

一新为周期的生物过程的联机预兆的监视和预言模型被建议用多方法 non-Gaussian 建模。这条途径的基本想法是多使用方法 non-Gaussian 当模特儿在一个周期的过程从每日的正常操作数据提取某主导的关键组分，并且随后把这些部件与预兆的统计过程监视相结合技术。建议预兆的监视方法被使用了在生物废水处理过程指责察觉和诊断，它基于强壮日报特征。结果显示出力量和优点连续法的建议预兆的监视用多方法预兆监视概念，它因此能为一个每日的监视过程并且也给很有用的概念的结果比另外的传统的监视方法启用过程差错的更快速的察觉。

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.