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Development of PCA-Based Fault Detection System Based on Various of NOC Models for Continuous-Based Process

Mohamad Yusup, Abd Wahab (2012) Development of PCA-Based Fault Detection System Based on Various of NOC Models for Continuous-Based Process. Faculty of Chemical and Natural Resources Engineering, Universiti Malaysia Pahang.

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Abstract

Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis. Currently, contribution plots are used as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly diagnose the fault, it just provides greater insight into possible causes and thereby narrow down the search. Hence, the cause of the faults cannot be found in a straightforward manner. Therefore, this study is conducted to introduce a new approach for detecting and diagnosing fault via correlation technique. The correlation coefficient is determined using multivariate analysis techniques, namely Principal Component Analysis (PCA). In order to overcome these problems, the objective of this research is to develop new approaches, which can improve the performance of the present conventional MSPC methods. The new approaches have been developed, the Outline Analysis Approach for examining the distribution of Principal Component Analysis (PCA) scores, the Correlation Coefficient Approach for detecting changes in the correlation structure within the variables. This research proposed PCA Outline Analysis Control Chart for fault detection. The result from the conventional method and ne approach were compared based on their accuracy and sensitivity. Based on the results of the study, the new approaches generally performed better compared to the conventional approaches, particularly the PCA Outline Analysis Control Chart.

Item Type: Undergraduates Project Papers
Additional Information: Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang – 2012
Uncontrolled Keywords: Multivariate analysis Technique Multivariate analysis Principal component analysis
Subjects: Q Science > QA Mathematics
Faculty/Division: Faculty of Chemical & Natural Resources Engineering
Depositing User: Ms Suriati Mohd Adam
Date Deposited: 11 Nov 2014 02:17
Last Modified: 03 Mar 2015 09:34
URI: http://umpir.ump.edu.my/id/eprint/7127
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