EMD-based online Filtering of Process Data. (May 2017) - Ma, Xi; Hu, Jinqiu; Zhang, Laibin
Abstract: In chemical industries, measurements corrupted by noise or outliers may affect operators' recognition of the current situation and lead them to make inappropriate control decisions. Data quality is a critical factor for process monitoring and fault diagnosis. A robust online filtering method (OLREMD 1 ) is proposed to implement online process data rectification with Empirical Mode Decomposition (EMD) as the basic algorithm. Tests with synthetic data show that OLREMD performs robustly with a lower sensitivity to parameters and improved performance on elimination of both noise and outliers. When applied to an industrial de-ethanizing column, OLREMD is shown to enhance the process monitoring performance. Graphical abstract: Highlights: A robust online data filtering of process data is proposed. The proposed method is an extension of EMD in the field of online process data rectification. It has a lower parametric sensitivity and improved performance on eliminating both noise and outliers. When applied on actual processes, it is practical to enhance process monitoring performance and improve data visual quality as well.