Fault Detection of Tennessee Eastman Process using Kernel Dissimilarity Scale Based Singular Spectrum Analysis

Abstract

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.