Estimating gas turbine compressor discharge temperature using Bayesian neuro-fuzzy modelling

Zhang, Yu, Martinez-Garcia, Miguel and Latimer, Anthony (2017) Estimating gas turbine compressor discharge temperature using Bayesian neuro-fuzzy modelling. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC2017), 5 - 8 October, 2017, Banff, Canada..

SMC17_0219_FI.pdf - Whole Document

Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive


The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational Bayesian Gaussian mixture model (VBGMM) techniques. A data-driven compressor model is built using ANFIS, and VBGMM is applied in the set-up stage to automatically select the number of input membership functions in the fuzzy system. The efficacy of the proposed BFNM approach is established through experimental trials of a sub-15MW gas turbine, and the results, from the model that is built using test bed data, are shown to be promising for estimating the compressor discharge temperatures on the gas turbine during commissioning.

Keywords:Bayesian neuro-fuzzy modeling, adaptive neuro-fuzzy inference system, variational Bayesian Gaussian mixture model, compressor discharge temperature, industrial gas turbine
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H714 Manufacturing Systems Maintenance
Divisions:College of Science > School of Engineering
ID Code:28741
Deposited On:26 Sep 2017 10:07

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