Abstract
Considering the data of Sequencing Batch Reactor (SBR) having the characteristics of non-Gaussian distribution and highly nonlinearity, this research applies Multi-way Kernel Independent Component Analysis (MKICA) to the on-line process monitoring of SBR. Meanwhile, a novel contribution analysis scheme named bar plot is developed for MKICA to diagnose faults. Above all, the three-dimensional data of SBR is expanded into two-dimensional by a new data expanding method; then, Kernel Principal Component Analysis (KPCA) is utilized to map the two-dimensional data into a high dimensional feature space, and make use of Independent Component Analysis (ICA) to extract Independent Components (ICs) in feature space; finally, if MKICA detects a fault occurs during on-line monitoring stage, the bar plot is used to identify the variables causing the fault. The method is successfully applied to an 80L lab-scale SBR. The experimental results demonstrate that, compared with traditional MICA, the proposed method exhibit better performance in fault detection and diagnose.
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