Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/31669
Title: Applications of Multiv Ariate Analysis Techniques for Fault Detection, Diagnosis and Isolation
Authors: PREM KRISHNAN
Keywords: Fault Detection, Fault Diagnosis and Identification, Correspondence Analysis, Weighted Pairwise Scatter Linear Discriminant Analysis
Issue Date: 19-Aug-2011
Source: PREM KRISHNAN (2011-08-19). Applications of Multiv Ariate Analysis Techniques for Fault Detection, Diagnosis and Isolation. ScholarBank@NUS Repository.
Abstract: In this study, powerful multivariate tools such as Principal Component Analysis (PCA), Partial Least Squares (PLS) and Correspondence Analysis (CA) are applied to the problem of fault detection, diagnosis and identification and their efficacies are compared. Specifically, CA which has been recently adapted and studied for FDD applications is tested for its robustness when compared to other conventional and familiar methods like PCA and PLS on simulated datasets from three industry-based, high-fidelity simulation models. This study demonstrates that CA can negotiate time varying dynamics in process systems as compared to the other methods. This ability to handle dynamics is also responsible for providing robustness to CA based FDD scheme. The results also confirm previous claims that CA is a good tool for early detection and concrete diagnosis of process faults. In, the second portion of this work, a new integrated CA and Weighted Pairwise Scatter Linear Discriminant Analysis method is proposed for fault isolation and identification. This tool tries to exploit the discriminative ability of CA to clearly distinguish between faults in the discriminant space and also predict if an abnormal event presently occurring in a plant is related to any previous faults that were recorded. The proposed method was found to give positive results when applied to simulated data containing faults that are either a combination of previously recorded failures or at intensities which are different from those previously recorded.
URI: http://scholarbank.nus.edu.sg/handle/10635/31669
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
KrishnanP.pdf2.26 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

287
checked on Dec 11, 2017

Download(s)

415
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.