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FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT

Nowadays, chemical plants are becoming complex due to high dependency among operational variables. Control loops are interdependent to optimize production. Therefore, the triggered floods of alarms complicate tracking the root fault among different process systems. Nevertheless,...

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Main Author: TAHOON, AMR IBRAHIM
Format: Thesis
Language:English
Published: 2020
Subjects:
TP Chemical technology
Online Access:http://utpedia.utp.edu.my/20417/1/Amr%20Ibrahim%20Tahoon_17007640.pdf
http://utpedia.utp.edu.my/20417/
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spelling my-utp-utpedia.204172021-08-18T23:00:25Z http://utpedia.utp.edu.my/20417/ FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT TAHOON, AMR IBRAHIM TP Chemical technology Nowadays, chemical plants are becoming complex due to high dependency among operational variables. Control loops are interdependent to optimize production. Therefore, the triggered floods of alarms complicate tracking the root fault among different process systems. Nevertheless, the alarm systems could have diverse failures leading to uncertainty in decision-making of Abnormal Situation Management (ASM). For these flooding and reliability issues in alarm systems, Bayesian Networks(BNs)are increasingly employed to model the relationships among the operational variables. However, fault inference using BN has structuring and learning issues for complex systems and little fault history respectively. 2020-05 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/20417/1/Amr%20Ibrahim%20Tahoon_17007640.pdf TAHOON, AMR IBRAHIM (2020) FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT. Masters thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
TAHOON, AMR IBRAHIM
FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT
description Nowadays, chemical plants are becoming complex due to high dependency among operational variables. Control loops are interdependent to optimize production. Therefore, the triggered floods of alarms complicate tracking the root fault among different process systems. Nevertheless, the alarm systems could have diverse failures leading to uncertainty in decision-making of Abnormal Situation Management (ASM). For these flooding and reliability issues in alarm systems, Bayesian Networks(BNs)are increasingly employed to model the relationships among the operational variables. However, fault inference using BN has structuring and learning issues for complex systems and little fault history respectively.
format Thesis
author TAHOON, AMR IBRAHIM
author_facet TAHOON, AMR IBRAHIM
author_sort TAHOON, AMR IBRAHIM
title FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT
title_short FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT
title_full FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT
title_fullStr FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT
title_full_unstemmed FAULT INFERENCE USINGENHANCEDBAYESIAN NETWORKSFOR ABNORMAL SITUATION MANAGEMENT
title_sort fault inference usingenhancedbayesian networksfor abnormal situation management
publishDate 2020
url http://utpedia.utp.edu.my/20417/1/Amr%20Ibrahim%20Tahoon_17007640.pdf
http://utpedia.utp.edu.my/20417/
_version_ 1739832750990426112
score 13.18916

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