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An adaptive regression adjusted monitoring and fault isolation scheme
Author(s) -
Liu Hancong,
Jiang Wei,
Tangirala Arun,
Shah Sirish
Publication year - 2006
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1020
Subject(s) - fault detection and isolation , multivariate statistics , fault (geology) , constant false alarm rate , computer science , alarm , dimension (graph theory) , scheme (mathematics) , regression , isolation (microbiology) , chart , regression analysis , false alarm , data mining , statistics , artificial intelligence , mathematics , machine learning , engineering , mathematical analysis , microbiology and biotechnology , seismology , aerospace engineering , pure mathematics , actuator , biology , geology
A new method is proposed in this work to detect and isolate faults in a system consisting of multivariate normal data. The proposed method reduces the variable dimension via a T 2 decomposition and combines multivariate monitoring and diagnosis in a unified approach. An adaptive regression adjusted (ARA) chart is introduced to utilize the diagnosis result in order to improve the monitoring scheme. Once an out‐of‐control alarm is detected, the fault isolation can be done directly. Copyright © 2007 John Wiley & Sons, Ltd.