Variational Bayesian modified adaptive mamdani fuzzy modelling for use in condition monitoring

Author

Yu Zhang ; Jun Chen ; Bingham, Chris ; Gordon, Timothy

Author_Institution

Sch. of Eng., Univ. of Lincoln, Lincoln, UK

fYear

2015

fDate

12-14 June 2015

Firstpage

1

Lastpage

5

Abstract

The paper proposes a new Adaptive Mamdani Fuzzy Model (AMFM) based system modelling methodology that improves on traditional Mamdani fuzzy rule based system (FRBS) techniques through use of alternative membership functions and a defuzzification mechanism that is `differentiable´, allowing a back error propagation (BEP) algorithm to refine the initial fuzzy model. Moreover, a variational Bayesian (VB) method is applied to simplify the results via automatic selection of the number of input rules so that redundant rules can be removed for the initial modelling phase. The efficacy of the proposed VB modified AMFM (VB-AMFM) approach is demonstrated through experimental trials using measurements from a compressor in an industrial gas turbine (IGT).

Keywords

Bayes methods; compressors; condition monitoring; fuzzy reasoning; fuzzy set theory; gas turbines; production engineering computing; variational techniques; AMFM-based system modelling methodology; BEP algorithm; FRBS techniques; IGT; Mamdani fuzzy rule based system techniques; VB-AMFM approach; VB-modified AMFM approach; alternative membership functions; automatic input rule selection; back error propagation algorithm; compressors; condition monitoring; defuzzification mechanism; differentiable mechanism; fuzzy model; industrial gas turbine; redundant rules; variational Bayesian modified adaptive Mamdani fuzzy modelling; Adaptation models; Bayes methods; Bismuth; Fuels; Gaussian mixture model; Turbines; Adaptive Mamdani fuzzy model; back error propagation; industrial gas turbine; variational Bayesian Gaussian mixture model;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 2015 IEEE International Conference on

Conference_Location

Shenzhen

Type

conf

DOI

10.1109/CIVEMSA.2015.7158608

Filename

7158608