Fault detection and diagnosis using correlation coefficients

Accurate process fault detection and diagnosis (FDD) at an early stage of a chemical process is very important to modern chemical plant in overcoming challenges such as strict requirements on product quality, low consumption of utility, environmentally friendly and safe operation. The use of the Con...

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Main Author:	Mak, Weng Yee
Format:	Thesis
Language:	English
Published:	2005
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/5320/1/MakWengYeeMFKKKSA2005.pdf
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id	my-utm-ep.5320
record_format	uketd_dc
spelling	my-utm-ep.53202020-07-22T04:07:06Z Fault detection and diagnosis using correlation coefficients 2005-12 Mak, Weng Yee TP Chemical technology Accurate process fault detection and diagnosis (FDD) at an early stage of a chemical process is very important to modern chemical plant in overcoming challenges such as strict requirements on product quality, low consumption of utility, environmentally friendly and safe operation. The use of the Contribution Plots (CP) for fault diagnosis in previous methods in Multivariate Statistical Process Control (MSPC) is not suitable since it is ambiguous due to no confidence limit in the CP. This research is to formulate a FDD algorithm based on MSPC via correlation coefficients. A fractionation column from a palm oil fractionation plant is chosen as the case study and the model of the case study is simulated in Matlab. Data collected with a process sampling time, TMSPC, of 4.6 hours and following the normal distribution are used as Nominal Operation Condition (NOC) data. Normal Correlation (NC), Principal Component Analysis (PCA) and Partial Correlation Analysis (PCorrA) are used to develop the correlation coefficients between the selected key process variables with the quality variables of interest in the process from the NOC data. Faults considered in the research are sensor faults, valve faults and controller faults generated in the fault data (OC). Shewhart Control Chart and Range Control Chart together with the developed correlation coefficients are used for fault detection and diagnosis. Results show that the method based on PCorrA (overall FDD efficiency = 100%) is more superior than the method based on NC (overall FDD efficiency = 67.82%) and the two analysis methods based on PCA (overall FDD efficiency = 67.82%). 2005-12 Thesis http://eprints.utm.my/id/eprint/5320/ http://eprints.utm.my/id/eprint/5320/1/MakWengYeeMFKKKSA2005.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Chemical and Natural Resources Engineering Faculty of Chemical and Natural Resources Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Mak, Weng Yee Fault detection and diagnosis using correlation coefficients
description	Accurate process fault detection and diagnosis (FDD) at an early stage of a chemical process is very important to modern chemical plant in overcoming challenges such as strict requirements on product quality, low consumption of utility, environmentally friendly and safe operation. The use of the Contribution Plots (CP) for fault diagnosis in previous methods in Multivariate Statistical Process Control (MSPC) is not suitable since it is ambiguous due to no confidence limit in the CP. This research is to formulate a FDD algorithm based on MSPC via correlation coefficients. A fractionation column from a palm oil fractionation plant is chosen as the case study and the model of the case study is simulated in Matlab. Data collected with a process sampling time, TMSPC, of 4.6 hours and following the normal distribution are used as Nominal Operation Condition (NOC) data. Normal Correlation (NC), Principal Component Analysis (PCA) and Partial Correlation Analysis (PCorrA) are used to develop the correlation coefficients between the selected key process variables with the quality variables of interest in the process from the NOC data. Faults considered in the research are sensor faults, valve faults and controller faults generated in the fault data (OC). Shewhart Control Chart and Range Control Chart together with the developed correlation coefficients are used for fault detection and diagnosis. Results show that the method based on PCorrA (overall FDD efficiency = 100%) is more superior than the method based on NC (overall FDD efficiency = 67.82%) and the two analysis methods based on PCA (overall FDD efficiency = 67.82%).
format	Thesis
qualification_level	Master's degree
author	Mak, Weng Yee
author_facet	Mak, Weng Yee
author_sort	Mak, Weng Yee
title	Fault detection and diagnosis using correlation coefficients
title_short	Fault detection and diagnosis using correlation coefficients
title_full	Fault detection and diagnosis using correlation coefficients
title_fullStr	Fault detection and diagnosis using correlation coefficients
title_full_unstemmed	Fault detection and diagnosis using correlation coefficients
title_sort	fault detection and diagnosis using correlation coefficients
granting_institution	Universiti Teknologi Malaysia, Faculty of Chemical and Natural Resources Engineering
granting_department	Faculty of Chemical and Natural Resources Engineering
publishDate	2005
url	http://eprints.utm.my/id/eprint/5320/1/MakWengYeeMFKKKSA2005.pdf
_version_	1747814578811568128

Fault detection and diagnosis using correlation coefficients

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