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- 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.
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- Singular spectrum analysis (SSA) has become a popular and widely used forecasting and pre-processing technique in time series analysis which is currently exploited in chemical process monitoring and fault detection. Given its increased application and superior performance in comparison to conventional multivariate methods such as Principal Component Analysis (PCA) and Wavelets and its nonlinear extensions, it is relevant to study the variants of SSA and its applications in process monitoring. In this study SSA is combined with Kernel Multidimensional Scaling called Kernel Dissimilarity Scale Based Singular Spectrum Analysis (KDSSA) and is used to detect the faults in the Tennessee Eastman Process (TEP). The methodology is focused on three particular faults which were not observable with conventional multivariate methods and its no nlinear extensions. The monitoring results showed that the proposed method is efficient in detecting those faults in reduced number of modes. A unified monitoring index combined T2 statistics with Q statistics is used to simplify the fault detection task.
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