relation: https://khub.utp.edu.my/scholars/7350/
title: Adaptive neuro-fuzzy inference system for performance health monitoring of industrial gas turbines
creator: Tahan-Bouria, M.
creator: Muhammad, M.
creator: Abdul Karim, Z.A.
description: Figuring whether or not a gas turbine is inclined to faults provides useful help for determining the required preventive action before failure happening. System identification is a discipline that learns the behavior of the healthy engine and employs it to predict the fault proneness. This study aims to discuss the performance of the Adaptive Neuro-Fuzzy Inference System (ANFIS) compared to the Artificial Neural Networks (ANNs) for the purpose of gas turbine performance identification. Toward this end, three system identification Bank of Networks (B-Ns), each corresponding to seven variables that are commonly measurable on most twin-shaft industrial gas turbine engines, are developed. The accuracy of the trained B-Ns are analyzed using the healthy performance data of an industrial 18.8 MW open-cycle offshore gas turbine. Making a comparison between the gained results from ANFIS and two various ANNs models revealed that ANFIS model is able to forecast various performance parameters with higher correlation coefficient and smaller MAPE values. © IEOM Society International. © IEOM Society International.
publisher: IEOM Society
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Tahan-Bouria, M. and Muhammad, M. and Abdul Karim, Z.A.  (2016) Adaptive neuro-fuzzy inference system for performance health monitoring of industrial gas turbines.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018372822&partnerID=40&md5=6d6f04889fca2cceebcf9e72f5f00f3b