relation: https://khub.utp.edu.my/scholars/7242/
title: Methodology for short-term performance prognostic of gas turbine using recurrent neural network
creator: Muhammad, M.
creator: Mohammadreza, T.B.
creator: Karim, Z.A.A.
description: The issue of performance prognosis has been a topic of considerable interest in industrial condition monitoring applications. An innovative data driven prognostic methodology has been introduced in the current study by utilizing artificial recurrent neural network (RNN) approach which intends to improve the capability of equipment performance prediction within a specified short time bound even with limited available data. The ability of the approach is demonstrated using condition monitoring parameters collected from a 20 MW industrial gas turbine. An appropriate selection and fusion of measured variables has been employed to feed RNN with the most influential performance information. The analysis demonstrated that the developed prognostic approach has a great potential to provide an accurate short term forecast of equipment performance which can be invaluable for maintenance strategy and planning. © 2015 IEEE.
publisher: IEEE Computer Society
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Muhammad, M. and Mohammadreza, T.B. and Karim, Z.A.A.  (2016) Methodology for short-term performance prognostic of gas turbine using recurrent neural network.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990540&doi=10.1109%2fIEEM.2015.7385755&partnerID=40&md5=9133895e419a5db8ad4ab7fc810ffef5
relation: 10.1109/IEEM.2015.7385755
identifier: 10.1109/IEEM.2015.7385755