Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant

Fault detection and monitoring is essentially important in wastewater treatment to ensure that safety, environmental regulations compliance, maintenance and operation of the Wastewater Treatment Plant (WWTP) are under control. Many researchers have developed methods in fault detection and monitoring...

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Main Author:	Mirin, Siti Nur Suhaila
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/78990/1/SitiNurSuhailaMFKE2014.pdf
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spelling	my-utm-ep.789902018-09-19T05:21:44Z Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant 2014 Mirin, Siti Nur Suhaila TK Electrical engineering. Electronics Nuclear engineering Fault detection and monitoring is essentially important in wastewater treatment to ensure that safety, environmental regulations compliance, maintenance and operation of the Wastewater Treatment Plant (WWTP) are under control. Many researchers have developed methods in fault detection and monitoring such as fuzzy logic, parameter estimation, neural network and Principal Component Analysis (PCA). In studies involving data and signal model approach, PCA is the most appropriate method used in this work. Besides when using PCA, the dimensionality of the data, noise and redundancy can be reduce. However, PCA is only suitable for data with mean constant or steady state data. The use of PCA can also increase false alarm and produce false fault in a plant such as WWTP. Modifications of PCA need to be done to overcome the problems and hence, enhanced methods of PCA are proposed in this work. The enhanced methods are Multiscale PCA (MSPCA) and Recursive PCA (RPCA), which are appropriate for offline monitoring test and online monitoring test, respectively. To see the effectiveness of the methods, they were applied into the european Co-operation in the field of Scientific and Technical Research (COST) simulation benchmark WWTP. The results from the simulation plant were then applied in a real WWTP, IWK Bunus Regional Sewage Treatment Plant (RSTP). The data of WWTP involved are Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and Nitrate (SNO). In analysis for both plants, faults were detected when the confidence limit is over 95% and confidence limits in the range of 90-95% were considered for alarm region in the data, using Hotelling's T2 and residual. Finally, simulation results of the proposed methods were compared and it was found that the enhanced methods of PCA (MSPCA and RPCA) were able to reduce false alarm and false fault in the analysis of fault detection by 70% for steady state influence and dynamic influence and hence provides more accurate results in detecting faults in the process data. 2014 Thesis http://eprints.utm.my/id/eprint/78990/ http://eprints.utm.my/id/eprint/78990/1/SitiNurSuhailaMFKE2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:107026 masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mirin, Siti Nur Suhaila Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant
description	Fault detection and monitoring is essentially important in wastewater treatment to ensure that safety, environmental regulations compliance, maintenance and operation of the Wastewater Treatment Plant (WWTP) are under control. Many researchers have developed methods in fault detection and monitoring such as fuzzy logic, parameter estimation, neural network and Principal Component Analysis (PCA). In studies involving data and signal model approach, PCA is the most appropriate method used in this work. Besides when using PCA, the dimensionality of the data, noise and redundancy can be reduce. However, PCA is only suitable for data with mean constant or steady state data. The use of PCA can also increase false alarm and produce false fault in a plant such as WWTP. Modifications of PCA need to be done to overcome the problems and hence, enhanced methods of PCA are proposed in this work. The enhanced methods are Multiscale PCA (MSPCA) and Recursive PCA (RPCA), which are appropriate for offline monitoring test and online monitoring test, respectively. To see the effectiveness of the methods, they were applied into the european Co-operation in the field of Scientific and Technical Research (COST) simulation benchmark WWTP. The results from the simulation plant were then applied in a real WWTP, IWK Bunus Regional Sewage Treatment Plant (RSTP). The data of WWTP involved are Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD) and Nitrate (SNO). In analysis for both plants, faults were detected when the confidence limit is over 95% and confidence limits in the range of 90-95% were considered for alarm region in the data, using Hotelling's T2 and residual. Finally, simulation results of the proposed methods were compared and it was found that the enhanced methods of PCA (MSPCA and RPCA) were able to reduce false alarm and false fault in the analysis of fault detection by 70% for steady state influence and dynamic influence and hence provides more accurate results in detecting faults in the process data.
format	Thesis
qualification_level	Master's degree
author	Mirin, Siti Nur Suhaila
author_facet	Mirin, Siti Nur Suhaila
author_sort	Mirin, Siti Nur Suhaila
title	Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant
title_short	Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant
title_full	Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant
title_fullStr	Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant
title_full_unstemmed	Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant
title_sort	fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2014
url	http://eprints.utm.my/id/eprint/78990/1/SitiNurSuhailaMFKE2014.pdf
_version_	1747818121068019712

Fault detection and monitoring system using enhanced principal component analysis for the application in wastewater treatment plant

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