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Process fault detection using time‐explicit Kiviat diagrams
Author(s) -
Wang Ray C.,
Edgar Thomas F.,
Baldea Michael,
Nixon Mark,
Wojsznis Willy,
Dunia Ricardo
Publication year - 2015
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15054
Subject(s) - process (computing) , visualization , fault detection and isolation , computer science , data mining , scale (ratio) , series (stratigraphy) , process control , fault (geology) , multivariate statistics , work in process , engineering , artificial intelligence , machine learning , programming language , paleontology , physics , quantum mechanics , seismology , biology , actuator , geology , operations management
Significant amounts of data are collected and stored during chemical process operations. The corresponding datasets are typically difficult to represent and analyze using traditional visualization methods. This article introduces time‐explicit Kiviat diagrams as a means to visualize the multidimensional time series data acquired from plant operations. This framework is then used to build multivariate control charts for large scale time series datasets, and to develop a fault detection mechanism that lends itself to real‐time implementation. The proposed methodology is applied to an industrial case study as well as to data obtained from the Tennessee Eastman process simulator, showing very good performance. © 2015 American Institute of Chemical Engineers AIChE J , 61: 4277–4293, 2015