Pattern Matching Using Correspondence Analysis

Katariya, A and Detroja, Ketan P (2013) Pattern Matching Using Correspondence Analysis. In: 1st American Control Conference, ACC 2013, 17-19, June 2013, Washington, DC; United States.

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Historical databases are usually filled with information about plant operation during normal as well as faulty situations. This wealth of information acquired over time, if analyzed properly, can be beneficial in two ways: i) identifying current plant operation status and ii) abnormal situation management if such abnormality had occurred earlier. Here, a new data driven, unsupervised pattern matching algorithm is presented. Effectiveness of the proposed pattern-matching algorithm stems from the proposed similarity factor that is based on correspondence analysis. Correspondence analysis is a multivariate statistical analysis and it has been shown to possess better diagnostic abilities compared to principal component analysis. An efficient pattern-matching algorithm should be able to discriminate between normal modes and fault modes of plant operation. Here the proposed algorithm is shown to have better discriminatory ability compared to PCA based similarity factor. A simulation case study involving the benchmark Tennessee Eastman Challenge problem is presented here to validate the efficacy of the proposed approach

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: fault diagnosis, pattern matching, statistical analysis
Subjects: Others > Electricity
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 24 Nov 2014 05:15
Last Modified: 05 Sep 2017 07:12
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