文章基本信息

标题：Fault Detection using Empirical Mode Decomposition based PCA and CUSUM with Application to the Tennessee Eastman Process
本地全文：下载
作者：Yuncheng Du ; Dongping Du
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：488-493
DOI：10.1016/j.ifacol.2018.09.377
语种：English
出版社：Elsevier
摘要：AbstractIn this work, a new algorithm is developed to identify stochastic faults in the Tennessee Eastman (TE) process, which integrates Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA), Cumulative Sum (CUSUM), and half-normal probability plot to detect three particular faults that could not be properly detected with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, sensitivity analysis, and fault detection. Measured variables are first decomposed into different scales using the EEMD-based PCA for extracting fault signatures, from which a subset of variables that are sensitive to faults are selected with the half-normal probability plot. Based on the specific variables, CUSUM-based statistics are further used for improved fault detection. The algorithm can successfully identify three particular faults in the TE process with small time delay.
关键词：KeywordsProcess monitoringcontrolprocess data analyticsstochastic faultssensitivity analysis