Distributed PCA Model for Plant-Wide Process Monitoring

For plant-wide process monitoring, most traditional multiblock methods are under the assumption that some process knowledge should be incorporated for dividing the process into several sub-blocks. However, the process knowledge is not always available in practice. In this case, the monitoring scheme should be implemented through an automatic way. This paper intends to develop a new sub-block principal component analysis (PCA) method for plant-wide process monitoring, which is named as distributed PCA model. By constructing sub-blocks through different directions of PCA principal components, the original feature space can be automatically divided into several subfeature spaces. The constructed distributed PCA models in different subspaces can not only reflect the local behavior of the process change but also enhance the monitoring performance through the combination of individual monitoring results. Both of the monitoring and fault diagnosis schemes are developed based on the distributed PCA model. Two simulation case studies are carried out, on the basis of which the effectiveness of the proposed method is confirmed.