Plant-Wide Process Fine-Scale Monitoring via Distributed Static Magnitude-Dynamic Difference

To monitor the plant-wide process finely, a novel distributed static magnitude-dynamic difference (DSM-DD) method is proposed in this article. First, given the high dimension of the collected data in the plant-wide process, the entire data space is divided into four orthogonal subspaces according to whether the data obey Gaussian distribution and whether it has serial correlation. Second, both the static magnitude and dynamic difference of the data in the four subspaces are used to build the monitoring model. In addition, not only the features within four subspaces are extracted but the correlation between different subspaces is also extracted to construct corresponding statistics. Third, all the statistics with physical significance are put together to form a statistic vector, and the local outlier factor method is used for constructing the synthetic index to determine whether the fault occurs. Finally, the superiority of the DSM-DD method is verified through a typical industrial case.