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Nonstationary fault detection and diagnosis for multimode processes
Author(s) -
Liu Jialin,
Chen DingSou
Publication year - 2010
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.11999
Subject(s) - fault detection and isolation , statistic , process (computing) , fault (geology) , computer science , gaussian , isolation (microbiology) , normal distribution , gaussian process , data mining , algorithm , mathematics , artificial intelligence , statistics , physics , geology , quantum mechanics , microbiology and biotechnology , biology , operating system , seismology , actuator
Fault isolation based on data‐driven approaches usually assume the abnormal event data will be formed into a new operating region, measuring the differences between normal and faulty states to identify the faulty variables. In practice, operators intervene in processes when they are aware of abnormalities occurring. The process behavior is nonstationary, whereas the operators are trying to bring it back to normal states. Therefore, the faulty variables have to be located in the first place when the process leaves its normal operating regions. For an industrial process, multiple normal operations are common. On the basis of the assumption that the operating data follow a Gaussian distribution within an operating region, the Gaussian mixture model is employed to extract a series of operating modes from the historical process data. The local statistic T 2 and its normalized contribution chart have been derived for detecting abnormalities early and isolating faulty variables in this article. © 2009 American Institute of Chemical Engineers AIChE J, 2010