Comprehensive Monitoring of Complex Industrial Processes with Multiple Characteristics
نویسندگان
چکیده
Traditional onefold data-driven methods for fault detection in complex process industrial systems with high-dimensional, linear, nonlinear, Gaussian, and non-Gaussian coexistence often have less than satisfactory monitoring performance because only a single distribution of variables is considered. To address this problem, hybrid model based on PCA-KPCA-ICA-KICA-BI (Bayesian inference) proposed, taking into account the advantages principal component analysis (PCA), kernel (KPCA), independent (ICA), (KICA) terms dimensionality reduction feature extraction. Foremost, paper proposed nonlinear evaluation method divided Gaussian linear blocks, blocks by using Jarque–Bera (JB) test discrimination method. Each division was monitored PCA-KPCA-ICA-KICA model, finally Bayesian fusion strategy study used to synthesize results each block. The helps evaluating variable features bettering performance. Ultimately, superiority verified through Tennessee Eastman (TE) Continuous Stirred Tank Reactor (CSTR) process, showed an average accuracy 85.91% model.
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ژورنال
عنوان ژورنال: International Journal of Chemical Engineering
سال: 2022
ISSN: ['1687-8078', '1687-806X']
DOI: https://doi.org/10.1155/2022/3054860