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. Note to Practitioners—To ensure higher product quality and safe operation of batch process, process monitoring has become an essential issue in the manufacturing process of a great deal of modern industrial plants. This study hence presents a novel monitoring method for complex batch processes with frequent operation characteristics. The present method can model strong non-Gaussian distribution data. For complex batch processes with frequent operations, the data follow serious non-Gaussian distribution due to random operations. Multiscale neighborhood normalization method is proposed to transfer non-Gaussian distribution data to Gaussian distribution so as to build multivariate statistical model. When online monitoring, the method shows fast model matching speed. Meanwhile taking into account the dynamic characteristics of the process, the MDPCA method is chosen to establish the model. Experimental results show that the present monitoring method can get higher detection rates, lower false alarm rates and shorter delays of fault capture than the conventional method.