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

机译：基于核相似度尺度的奇异谱分析的田纳西伊士曼过程故障检测

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摘要

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 T2statistics withQstatistics is used to simplify the fault detection task.
机译：奇异频谱分析（SSA）已成为时间序列分析中一种流行且广泛使用的预测和预处理技术，该技术目前已用于化学过程监控和故障检测中。与传统的多元方法（例如主成分分析（PCA）和小波）及其非线性扩展相比，与传统的多变量方法相比，它的应用有所增加，并且具有优越的性能，因此研究SSA的变体及其在过程监控中的应用具有重要意义。在这项研究中，SSA与称为多维核不相似标度的奇异频谱分析（KDSSA）的内核多维标度相结合，用于检测田纳西伊士曼过程（TEP）中的故障。该方法的重点是三个特定的故障，这是常规多元方法无法观察到的，并且没有非线性扩展。监测结果表明，所提出的方法能够有效地检测出模式数量减少的故障。将T2statistics与Qstatistics结合使用的统一监视索引可简化故障检测任务。

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