Performance Evaluation of Independent Component Analysis-Based Fault Detection Using Measurements Corrupted with Noise

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The detection of sensor faults has proven to be easier through data-driven methods which rely on historical data collected from sensors that are placed at various locations in a process plant. Since the distribution of industrial process variables is random and non-Gaussian, the independent component analysis (ICA) method has been better suited for fault detection (FD) problems. Whenever data comes with any level of noise, there is difficulty in separating useful information, which hence degrades the monitoring quality of an FD strategy. In this paper, the robustness of FD strategies is assessed for different noise realizations of sensor data using stochastic simulations. The main objective of this work is to demonstrate that ICA-based FD strategies are more robust for different noise levels in comparison with principal component analysis (PCA). The ICA modeling algorithm is improved to avoid random initialization of a de-mixing orthogonal matrix during computation of independent components. Two case studies are considered for evaluating the robustness of FD strategies: a simulated quadruple tank process and a simulated distillation column process. Comparisons have been carried out between ICA, dynamic ICA, modified ICA and PCA strategies for different sensor noise levels. The simulation results reveal that ICA-based FD strategies over-perform PCA FD strategy in monitoring sensor faults for different levels of noise.