Improved nonlinear process monitoring based on ensemble KPCA with local structure analysis

Abstract A new nonlinear process monitoring algorithm called ensemble local kernel principal component analysis (ELKPCA) is proposed. Conventionally, the performance of kernel-based model depends on the width parameter selected empirically in Gaussian kernel function, which means a single parameter corresponds to a single model. Once a poor width parameter is decided, a single kernel-based model may be only effective for part faults. As a typical kernel-based method, the local kernel principal component analysis (LKPCA), considering both global and local structure information of the original data, faces the problem of width parameter selection as well. Since a single model is one-sided, an available way is to combine different single models and take advantage of them. The ensemble kernel principal component analysis (EKPCA) uses single KPCA models as its sub-models and ensemble learning approach is used to combine them. Due to inherit drawbacks from KPCA models, EKPCA only preserves global structure information of data, but ignores important local structure information. In this paper, to solve the above issues, both LKPCA and EKPCA is unified in the proposed framework. First, single LKPCA models are chosen instead and combined by using ensemble learning strategy. Then two monitoring statistics are turned into fault probabilities through Bayesian inference approach and weighted combination strategy, which makes the monitoring behavior easier and more clear. The result shows that ELKPCA model can not only take advantage of sub-LKPCA models effectively, allowing it selecting width parameter more easily and stably, but also retain both global and local structure information from input data by introducing the local structure analysis in the EKPCA model. Case studies on synthetic example and Tennessee Eastman process demonstrate the proposed method outperforms LKPCA and EKPCA and enhances monitoring performance significantly.