Fault Detection of Non-Gaussian Processes Based on Model Migration

In this paper, a new modeling approach is proposed for common and specific feature extraction. The original space of a mode can be separated into two different parts, namely, the common and specific ones. There are both non-Gaussian similarity and dissimilarity in the underlying correlations of different modes. After two different non-Gaussian blocks are separated, one can obtain the common and specific blocks, respectively. They play different roles in industrial batch processes, which are referred to as repetitive and complementary effects, respectively. Then, the common block and specific block are analyzed. A new multiblock monitoring method is proposed and the monitoring process is carried out in each block. The proposed method is applied to process monitoring of a continuous annealing process. Application results indicate that the proposed approach effectively captures the non-Gaussian relations to build the process model and improves the detection ability.