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Structural Fault Detection Using Dynamic Principal Component Analysis (DPCA)

Kalaichelvan, Mohana Rooparn (2014) Structural Fault Detection Using Dynamic Principal Component Analysis (DPCA). IRC, Universiti Teknologi PETRONAS. (Unpublished)

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Abstract

ABSTRACT Principal component analysis (PCA) is a well-known data dimensionality reduction technique that has been used to detect faults during the operation of industrial processes. A modification to this is the Dynamic Principal Component Analysis (DPCA) which takes into account serial correlations for rapid sampling and detection of faults. This method, although being studied for its fault detection capabilities, has not yet been widely tested and proved to detect a particular type of faults called structural faults. In this paper, a dynamic model of a Continuous Stirred Tank Reactor (CSTR) is built using the MATLAB software and used to generate a sample of base data and another sample of data with structural faults present. Further on in this project, the two data sets would be compared and tested using T2-statistics and Q-statistics method to identify the faults occurring in the system and study the difference in performances of these methods. Q-statistics which quantifies variations in the residual space is more sensitive but less robust to the faults than the T2- statistics quantifying the variations in the score or state space. Faster fault detection via DPCA is also achieved in T2-statistics whereas results from Q-statistics are inconclusive.

Item Type: Final Year Project
Academic Subject : Academic Department - Chemical Engineering - Process System Engineering
Subject: T Technology > TP Chemical technology
Divisions: Engineering > Chemical
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 27 Jan 2015 11:36
Last Modified: 25 Jan 2017 09:36
URI: http://utpedia.utp.edu.my/id/eprint/14485

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