Online process monitoring scheme for fault detection based on Independent Component Analysis (ICA) and Local Outlier Factor (LOF)

Process monitoring in manufacturing field such as chemistry and pharmacy is valuable to reduce the risk of the accidents and to improve the efficiency of the process. Due to the increased size and complicated technology of the modern plants, computer-aided process monitoring has been introduced. Nevertheless, the existing methods for computer-aided process monitoring such as Multivariate Statistical Process Control (MSPC) based on Principal Component Analysis (PCA) or Independent Component Analysis (ICA) present several limits in accurate fault detection of process. To overcome these limits, we introduce a novel online computer-aided process monitoring scheme based on ICA with Local Outlier Factor (LOF). The proposed scheme adopts LOF as the monitoring statistic. By utilizing LOF, the limits of existing related works using MSPC were successfully resolved. The scheme was tested by widely used benchmark dataset of Tennessee Eastman process. The results showed the effectiveness of the proposed scheme.