Data reduction and fault diagnosis using principle of distributional equivalence

Detroja, Ketan P and Gudi, R D and Patwardhan, S C (2011) Data reduction and fault diagnosis using principle of distributional equivalence. In: International Symposium on Advanced Control of Industrial Processes, 23-26, May 2011, Hangzhou, Zhejiang; China.

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Historical data based fault diagnosis methods exploit two key strengths of the multivariate statistical tool being used: i) data compression ability, and ii) discriminatory ability. It has been shown that correspondence analysis (CA) is superior to principal components analysis (PCA) on both these counts[1], and hence is more suited for the task of fault detection and isolation(FDI). In this paper, we propose a methodology for fault diagnosis that can facilitate significant data reduction as well as better discrimination. The proposed methodology is based on the principle of distributional equivalence (PDE). The PDE is a property unique to CA and can be very useful in analyzing large datasets. The principle, when applied to historical data sets for FDI, can significantly reduce the data matrix size without significantly affecting the discriminatory ability of the CA algorithm. The data reduction ability of the proposed methodology is demonstrated using a simulation case study involving benchmark quadruple tank laboratory process. The above aspect is also validated for large scale system using benchmark Tennessee Eastman process simulation case study.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Correspondence analysis; Data matrices; Fault detection and isolation; Fault diagnosis method; Historical data; Laboratory process; Large datasets; Principal components analysis; Statistical tools; Tennessee Eastman process
Subjects: Chemical Engineering > Biochemical Engineering
Physics > Electricity and electronics
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 30 Oct 2014 09:35
Last Modified: 05 Sep 2017 07:13
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