Multiscale Neighborhood Normalization-Based Multiple Dynamic PCA Monitoring Method for Batch Processes With Frequent Operations

This paper presents a novel multiscale neighborhood normalization-based multiple dynamic principal component analysis (MNN-MDPCA) method to detect the fault in complex batch processes with frequent operations. Since the difference between batches is larger under random frequent operations according to phase, the corresponding monitoring model should be changed accordingly. However, the data quantity is small under a single operation at each phase, the data with similar operations can be clustered together. Due to frequent operations, the data clustered follows non-Gaussian distribution. A normalization strategy called MNN is proposed to complete Gaussian distribution conversion so as to build multivariate statistical model. Subsequently, MDPCA is used to model the multioperation industry processes. Finally, to test the modeling and monitoring performance of the proposed method, a numerical example and the ladle furnace (LF) steelmaking process case are provided, where the comparison with Gaussian mixture model and MDPCA-based results is covered.