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Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system

Vibration in a steam turbine-generator is one of the many default problems, similar to thrust, crack and low or high speeds, all of which causes damage to the steam turbine if leaves unprotected . It leads to accidents and damages, when overcome the limit of alarm or danger zones. The...

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Main Authors: Moneer, Ali Lilo, Abdul Latiff, Liza, Yousif, I. Almashhadany, Abu, Aminudin
Format: Article
Published: Maxwell Scientific Publication Corporation 2016
Subjects:
T Technology
Online Access:http://eprints.utm.my/id/eprint/68489/
http://dx.doi.org/10.19026/rjaset.12.2 687
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spelling my.utm.684892017-11-20T08:52:09Z http://eprints.utm.my/id/eprint/68489/ Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system Moneer, Ali Lilo Abdul Latiff, Liza Yousif, I. Almashhadany Abu, Aminudin T Technology Vibration in a steam turbine-generator is one of the many default problems, similar to thrust, crack and low or high speeds, all of which causes damage to the steam turbine if leaves unprotected . It leads to accidents and damages, when overcome the limit of alarm or danger zones. The protection of steam turbine generators from danger leads to reduced maintenances and augmented stability of power generation. The main proposal of this study is to identify and classify vibrations in alarm and shutdown zones, it is also intended to produce a smooth signal that can be used to adjust control value, which influences the vibration value during the start-up and power generation. We compared the series and parallel-connected Neural Network (N N) that is related to time and error to identify vibration acceleration signals and flow by sleep fuzzy sugeno s ystem, which are designed and simulated in MATLAB. The results showed that parallel-connected NN is superior to its series-connected counterpart with vibration signals, where the Neural-Sleep-Fuzz y system and the NSFS robust system produces zero voltages when it lacks vibrations, more so after receiving ali near signal to influence nonlinear signals of vibration. This study concluded that the Artificial Intelligent (AI) syste m with sleep fuzzy sugeno system can be implementing to classify the fault of optimal vibration signal limitation an d check the suitable treatment for this fault. Also, the analysis of results can conclude that using parallel NN is fast er and more accurate compared to series NN connection. Maxwell Scientific Publication Corporation 2016-01-03 Article PeerReviewed Moneer, Ali Lilo and Abdul Latiff, Liza and Yousif, I. Almashhadany and Abu, Aminudin (2016) Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system. Research Journal in Applied Sciences, Engineering and Technology, 12 (5). pp. 589-598. ISSN 2040-7459 http://dx.doi.org/10.19026/rjaset.12.2 687 DOI:10.19026/rjaset.12.2 687
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/
topic T Technology
spellingShingle T Technology
Moneer, Ali Lilo
Abdul Latiff, Liza
Yousif, I. Almashhadany
Abu, Aminudin
Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
description Vibration in a steam turbine-generator is one of the many default problems, similar to thrust, crack and low or high speeds, all of which causes damage to the steam turbine if leaves unprotected . It leads to accidents and damages, when overcome the limit of alarm or danger zones. The protection of steam turbine generators from danger leads to reduced maintenances and augmented stability of power generation. The main proposal of this study is to identify and classify vibrations in alarm and shutdown zones, it is also intended to produce a smooth signal that can be used to adjust control value, which influences the vibration value during the start-up and power generation. We compared the series and parallel-connected Neural Network (N N) that is related to time and error to identify vibration acceleration signals and flow by sleep fuzzy sugeno s ystem, which are designed and simulated in MATLAB. The results showed that parallel-connected NN is superior to its series-connected counterpart with vibration signals, where the Neural-Sleep-Fuzz y system and the NSFS robust system produces zero voltages when it lacks vibrations, more so after receiving ali near signal to influence nonlinear signals of vibration. This study concluded that the Artificial Intelligent (AI) syste m with sleep fuzzy sugeno system can be implementing to classify the fault of optimal vibration signal limitation an d check the suitable treatment for this fault. Also, the analysis of results can conclude that using parallel NN is fast er and more accurate compared to series NN connection.
format Article
author Moneer, Ali Lilo
Abdul Latiff, Liza
Yousif, I. Almashhadany
Abu, Aminudin
author_facet Moneer, Ali Lilo
Abdul Latiff, Liza
Yousif, I. Almashhadany
Abu, Aminudin
author_sort Moneer, Ali Lilo
title Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
title_short Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
title_full Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
title_fullStr Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
title_full_unstemmed Identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
title_sort identify and classify vibration signal for steam turbine based on neural sleep fuzzy system
publisher Maxwell Scientific Publication Corporation
publishDate 2016
url http://eprints.utm.my/id/eprint/68489/
http://dx.doi.org/10.19026/rjaset.12.2 687
_version_ 1643655950914027520
score 13.211869

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