LAUSR

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.