Abstract
An adaptive monitoring method is proposed to detect the fault in complex batch processes with multiple operations. The method firstly starts with limited modeling data types to build the model, and then updates initial model data by collecting new normal batches. The specific process includes stage division, the conversion of non-Gaussian to Gaussian of process data and the construction of clustering model. Then, an adaptive updating algorithm, based on the selection of clustering models and up-down monitoring method, is developed for model updating to collect additional information from new normal batch data. The proposed method is illustrated through the 120t ladle furnace (LF) steelmaking process. Simulation results have shown the effectiveness of the proposed approaches compared to MPCA.
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