Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine

The present study aims to investigate the use of Artificial Neural Networks (ANN) for the performance-based condition monitoring of indusrial gas turbine engines. Toward this end, a health assessment tool is presented by developing a Multi-Nets ANN model. A number of key performance parameters that...

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Main Authors: Tahan, M., Sarwar, U., Muhammad, M., Abdul Karim, Z.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.25338 /
Published: Asian Research Publishing Network 2016
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009172873&partnerID=40&md5=09a296449bd86af6d7efb602c3add765
http://eprints.utp.edu.my/25338/
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spelling utp-eprints.253382021-08-27T12:58:48Z Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine Tahan, M. Sarwar, U. Muhammad, M. Abdul Karim, Z.A. The present study aims to investigate the use of Artificial Neural Networks (ANN) for the performance-based condition monitoring of indusrial gas turbine engines. Toward this end, a health assessment tool is presented by developing a Multi-Nets ANN model. A number of key performance parameters that are commonly measurable on the most industrial gas turbines are monitored and their associated neural networks for the healthy condition are trained. Three-layer feed-forward configuaration is chosen to construct the networks, the Levenberg-Marquardt algorithm is used as the training function, and the k-fold cross-validation process is employed to obtain the optimum number of neurons in the hidden layers. The model is developed and tested using the gas path performance data collected from an 18.7 MW twin-shaft industrial gas turbine. A special attention is also devoted to the system theory interpretation in order to evaluate the effect of the input neurons on each output of the Multi-Nets. To that end, the sensitivity analysis is formulated using derivatives based on an interpretation of the neural network's weights. © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved. Asian Research Publishing Network 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009172873&partnerID=40&md5=09a296449bd86af6d7efb602c3add765 Tahan, M. and Sarwar, U. and Muhammad, M. and Abdul Karim, Z.A. (2016) Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine. ARPN Journal of Engineering and Applied Sciences, 11 (24). pp. 14269-14274. http://eprints.utp.edu.my/25338/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description The present study aims to investigate the use of Artificial Neural Networks (ANN) for the performance-based condition monitoring of indusrial gas turbine engines. Toward this end, a health assessment tool is presented by developing a Multi-Nets ANN model. A number of key performance parameters that are commonly measurable on the most industrial gas turbines are monitored and their associated neural networks for the healthy condition are trained. Three-layer feed-forward configuaration is chosen to construct the networks, the Levenberg-Marquardt algorithm is used as the training function, and the k-fold cross-validation process is employed to obtain the optimum number of neurons in the hidden layers. The model is developed and tested using the gas path performance data collected from an 18.7 MW twin-shaft industrial gas turbine. A special attention is also devoted to the system theory interpretation in order to evaluate the effect of the input neurons on each output of the Multi-Nets. To that end, the sensitivity analysis is formulated using derivatives based on an interpretation of the neural network's weights. © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.
format Article
author Tahan, M.
Sarwar, U.
Muhammad, M.
Abdul Karim, Z.A.
spellingShingle Tahan, M.
Sarwar, U.
Muhammad, M.
Abdul Karim, Z.A.
Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine
author_sort Tahan, M.
title Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine
title_short Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine
title_full Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine
title_fullStr Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine
title_full_unstemmed Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine
title_sort modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine
publisher Asian Research Publishing Network
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009172873&partnerID=40&md5=09a296449bd86af6d7efb602c3add765
http://eprints.utp.edu.my/25338/
_version_ 1741196958560681984
score 11.62408

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