标题：
Monitoring Nonstationary Processes Using Stationary Subspace Analysis and Fractional Integration Order Estimation
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所属单位：
航空学院
发表刊物：
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
关键字：
PRINCIPAL COMPONENT ANALYSIS DYNAMIC MULTIVARIATE PROCESSES FAULT-DETECTION TIME-SERIES UNIT-ROOT DIAGNOSIS COINTEGRATION ALGORITHMS PCA
摘要：
This article introduces a framework to monitor complex dynamic and mildly nonstationary processes that are driven by a set of latent factors that can have different integration orders. The framework (i) relies on a novel deflation-based stationary subspace analysis that extracts latent source variables from recorded data sets in an iterative manner and (ii) utilizes the exact local Whittle estimator to calculate the fractional integration orders of the extracted source variables. The framework is embedded within a multivariate time-series structure to model the dynamic characteristics of the latent factors and to remove serial correlation in order to construct univariate monitoring statistics. A numerical and an industrial case study show that this framework is capable of modeling dynamic and mildly nonstationary variable inter-relationships that can have different integration orders.
ISSN号：
0888-5885
是否译文：
否
发表时间：
2019-04-24
合写作者：
Lin, Yuanling,Kruger, Uwe,Gu, Fengshou,Ball, Andrew
通讯作者：
陈前
发表时间：
2019-04-24
学位：工学博士学位
职称：教授
所在单位：航空学院