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

机译：使用经验模式分解和奇异谱分析的过程监控和故障检测

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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.
机译：本文提出了一种基于奇异谱分析（SSA）和经验模态分解（EMD）的数据驱动多元多尺度统计过程监控方法，用于化工过程系统的故障检测。 SSA使用轨迹矩阵的特征值提取过程信号的趋势，而EMD使用固有模式函数（IMF）通过筛选过程捕获信号趋势。从工业和模拟案例研究获得的结果表明，SSA和常规的多元统计过程监控技术（例如主成分分析（PCA））无法有效地提取信号中的非平稳和非线性趋势。作为替代方案，在本研究中，在使用PCA进行过程监控之前，将SSA与EMD分解相结合。通过将两种技术结合起来，EMD分析非平稳和非线性信号的效率提高了线性SSA技术的性能。实验和仿真结果还表明，使用EMD进行故障检测可与组合技术媲美。

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