基于TTGNPE算法的间歇过程监控

针对间歇过程中三维数据展开为二维造成的部分信息丢失以及数据的全局和局部结构可能发生的变化,提出一种基于张量分解的时序扩展全局局部邻域保持嵌入(TTGNPE)算法.首先利用TTGNPE算法直接处理间歇过程中的三维数据,以避免因展开为二维而造成的信息丢失;然后,将近邻流形嵌入并引入数据空间的全局和局部结构保持中,充分提取数据的局部和全局特征信息;最后,结合移动数据窗技术来处理过程的动态时变性,检测到故障后用贡献图法诊断出故障变量.通过青霉素发酵过程验证了所提出的算法对间歇过程故障检测与诊断的优越性.

Batch process monitoring based on TTGNPE algorithm

Three-dimension data of batch process unfolded two-dimension data can cause some information loss, and the global and local structures of data may change in the process. Therefove, a temporal extension global-local neighborhood preserving embedding based on tensor factorization(TTGNPE) algorithm is proposed, which can deal directly with three-dimension data to avoid folding information loss. Neighborhood preserving embedding is introduced in the global and local structure preserving of the data space to fully extract global and local feature information of data. By combining moving the data window to handle process dynamic time-variance, the contribution plot method is applied to diagnose fault variables after a failure being detected. The superiority of the proposed algorithm on the penicillin fermentation process is verified for the fault detection and diagnosis of the batch process.