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首页> 外文期刊>Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers >Detecting faults in process systems with singular spectrum analysis
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Detecting faults in process systems with singular spectrum analysis

机译：检测故障过程中系统与奇异光谱分析

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摘要

In this study, process monitoring based on signal decomposition by use of singular spectrum analysis (SSA) is considered. SSA makes use of adaptive basis functions to decompose a time series into multiple components that may be periodic, aperiodic or random. Two variants of SSA are considered in this investigation. In the first, the conventional approach is used based on latent variables extracted from the covariances of the lagged trajectory matrix of the process variables. The second approach is identical to the first approach, except that the covariances of the lagged trajectory matrices are replaced by Euclidean distance dissimilarities to decompose the variables into additive components. These components are subsequently monitored and the merits of the two approaches are considered on the basis of two case studies using simulated nonlinear data and data from the benchmark Tennessee Eastman process. (C) 2016 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
机译：在这项研究中,基于信号的过程监控通过利用奇异谱分解分析(SSA)被认为是。自适应基函数分解系列可能为多个组件周期、非周期或随机的。SSA被认为是在这个调查。首先,传统的方法是基于使用潜变量提取自协方差的滞后过程的轨迹矩阵变量。第一种方法,除了协方差滞后的轨迹矩阵所取代欧氏距离分解不同为添加剂的组件的变量。随后组件和监测这两种方法的优点被认为是两个案例研究使用模拟的基础非线性数据和数据的基准田纳西伊士曼的过程。化学工程师。保留所有权利。

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