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Sensor placement optimization for multiple fault detection using bayesian approach

Monitoring, diagnosis and prognosis in a complex system required multiple and different type of sensors to extract data form their structures. Sensors measure physical quantity of parameters of various levels of the system for preventing faults of a system. Uncertainties inherent in sensors cause un...

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Main Author: Davoudifar, Farshad
Format: Thesis
Language:English
Published: 2013
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/38023/1/FarshadDavoudifarMFKE2013.pdf
http://eprints.utm.my/id/eprint/38023/
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spelling my.utm.380232018-04-12T05:41:21Z http://eprints.utm.my/id/eprint/38023/ Sensor placement optimization for multiple fault detection using bayesian approach Davoudifar, Farshad TK Electrical engineering. Electronics Nuclear engineering Monitoring, diagnosis and prognosis in a complex system required multiple and different type of sensors to extract data form their structures. Sensors measure physical quantity of parameters of various levels of the system for preventing faults of a system. Uncertainties inherent in sensors cause uncertainty issue in data sets. Data extraction of sensors simultaneously brings with overlapping issue in the system. Whereas, current methods are considered that there are non-overlapping in the system or uncertainties of sensors are ignored. However, reducing cost or physical and technological limitations cause to constraint the number of sensors in the systems. The right placement of sensors affects on the reliability and safety of the system. This dissertation presents an application of Bayesian approach on sensor placement optimization that covers overlapping and uncertainties issues. It also recommends the best possibility placement combination of sensors in a system. The Bayesian Network methodology is introduced with likelihood function for on-demand systems. The proposed algorithm generates evidence sets on-demand for overlapping and uncertainty data. The algorithm calculate information matrix for various possible sensor placement that the most expected information gain show the best location of sensors. This approach applies on car engine that has various faults in the performance of engine with the limited number of sensors. Finally, algorithm presents the best possible placement of sensor 2013-06 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/38023/1/FarshadDavoudifarMFKE2013.pdf Davoudifar, Farshad (2013) Sensor placement optimization for multiple fault detection using bayesian approach. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Davoudifar, Farshad
Sensor placement optimization for multiple fault detection using bayesian approach
description Monitoring, diagnosis and prognosis in a complex system required multiple and different type of sensors to extract data form their structures. Sensors measure physical quantity of parameters of various levels of the system for preventing faults of a system. Uncertainties inherent in sensors cause uncertainty issue in data sets. Data extraction of sensors simultaneously brings with overlapping issue in the system. Whereas, current methods are considered that there are non-overlapping in the system or uncertainties of sensors are ignored. However, reducing cost or physical and technological limitations cause to constraint the number of sensors in the systems. The right placement of sensors affects on the reliability and safety of the system. This dissertation presents an application of Bayesian approach on sensor placement optimization that covers overlapping and uncertainties issues. It also recommends the best possibility placement combination of sensors in a system. The Bayesian Network methodology is introduced with likelihood function for on-demand systems. The proposed algorithm generates evidence sets on-demand for overlapping and uncertainty data. The algorithm calculate information matrix for various possible sensor placement that the most expected information gain show the best location of sensors. This approach applies on car engine that has various faults in the performance of engine with the limited number of sensors. Finally, algorithm presents the best possible placement of sensor
format Thesis
author Davoudifar, Farshad
author_facet Davoudifar, Farshad
author_sort Davoudifar, Farshad
title Sensor placement optimization for multiple fault detection using bayesian approach
title_short Sensor placement optimization for multiple fault detection using bayesian approach
title_full Sensor placement optimization for multiple fault detection using bayesian approach
title_fullStr Sensor placement optimization for multiple fault detection using bayesian approach
title_full_unstemmed Sensor placement optimization for multiple fault detection using bayesian approach
title_sort sensor placement optimization for multiple fault detection using bayesian approach
publishDate 2013
url http://eprints.utm.my/id/eprint/38023/1/FarshadDavoudifarMFKE2013.pdf
http://eprints.utm.my/id/eprint/38023/
_version_ 1643650211835281408
score 13.211869

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