Fault Detection using Empirical Mode Decomposition based PCA and CUSUM with Application to the Tennessee Eastman Process

In this work, a new algorithm is developed to identify stochastic faults in the Tennessee Eastman (TE) process, which integrates Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA), Cumulative Sum (CUSUM), and half-normal probability plot to detect three particular faults that could not be properly detected with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, sensitivity analysis, and fault detection. Measured variables are first decomposed into different scales using the EEMD based PCA for extracting fault signatures, from which a subset of variables that are sensitive to faults are selected with the half-normal probability plot. Based on the specific variables, CUSUM-based statistics are further used for improved fault detection. The algorithm can successfully identify three particular faults in the TE process with small time delay. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.