Process Monitoring and Fault Detection using Empirical Mode Decomposition and Singular Spectrum Analysis

Abstract In this study, a new data-driven multivariate multiscale statistical process monitoring method based on singular spectrum analysis (SSA) and empirical mode decomposition (EMD) is proposed for fault detection in chemical process systems. SSA extracts the trends of process signals using the eigenvalues of trajectory matrices while EMD uses the intrinsic mode functions (IMFs) to capture the signal trends through sifting process. The results obtained from the industrial and simulated case studies showed that SSA and conventional multivariate statistical process monitoring technique such as principal component analysis (PCA) failed to extract the nonstationary and nonlinear trends in the signal effectively. As an alternative, in this study, SSA is combined with EMD decomposition prior to the process monitoring procedure using PCA. The efficiency of EMD in analyzing the nonstationary and nonlinear signals enhanced the performance of linear SSA techniques by combining the two techniques in this study. Experimental and simulation results also revealed that fault detection using EMD is comparable to the combined technique.