| Summary: | In order to mine the local behavior and dynamic characteristic of batch process data for effective process monitoring, a two-dimensional localized dynamic support vector data description (TLDSVDD) method is proposed in this article. The main contributions of the proposed method include three aspects. Firstly, considering that batch process variables may behave differently at each operation stage, a two-dimensional localization strategy is designed to mine the local behaviors of process data from the perspective of the variable dimension and the sample dimension. Secondly, for each local data segment, the slow feature analysis is applied to build the local dynamic sub-models, which can monitor the static and dynamic process changes simultaneously. Lastly, the model ensemble strategy based on Bayesian inference is employed and two holistic monitoring statistics are developed to indicate the process running status. The proposed method not only extracts the local process behaviors, but also determines whether the process fault belongs to the dynamic or static change. Finally, one case study on the simulated industrial batch process is carried out to exhibit the method performance.
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