Process Monitoring via Key Principal Components and Local Information Based Weights

There are two problems in principal component analysis (PCA), which is widely employed in multivariate statistical process monitoring. On one hand, principal components selection according to the variance of the normal training dataset cannot represent the amount of fault information included in the online data. Thus, the useful fault information loss would exist and leads to poor monitoring performance. On the other hand, although the fault information contained in every principal component is different and the principal components are treated equally in traditional PCA-based methods. Then, some useful fault information would be suppressed. In order to reduce the dimension and preserve every original variable information as complete as possible at the same time, this paper selects key principal components using our previously proposed full variable expression method. Moreover, according to the accumulated reachability distance of the online data relative to that of the offline training data, those key principal components with large accumulated reachability distance are emphasized and weighted. Finally, the statistics are constructed to monitor the operation status, and the process monitoring performance of the proposed method is evaluated under an industrial process.