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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. 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.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

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.

Item Type:Article
Impact Factor:cited By 0
ID Code:25338
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 12:58
Last Modified:27 Aug 2021 12:58

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