非高斯建模方法的间歇过程的在线预测监控和预测模型

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.

Online predictive monitoring and prediction model for a periodic process through multiway non-Gaussian modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Ganssian modeling. The basic idea of this approach is to use multiway non-Gaussian mod-eling to extract some dominant key components from daily normal operation data in a periodic process, and subse-quently combining these components with predictive statistical process monitoring techniques. The proposed pre-dictive 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 pro-posed 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 detec-tion of the process fault than other traditional monitoring methods.