Reliability Modeling Using an Adaptive Neuro-Fuzzy Inference System: Gas Turbine Application

Recently, the development of the industry requires monitoring and follow-up of the working conditions of the facilities, to determine the reliability, availability, and durability of these systems, for objectively estimating the service life of these installations with reduced maintenance costs. In this sense, this work proposes a novel approach to reliability modeling, to determine failure assessment indicators based on an adaptive neuro-fuzzy inference system applied on a gas turbine. This is in order to describe the behavior of this rotating machine and to estimate their operating safety parameters, to improve its performance in terms of maintainability, availability, and operational safety with effective durability. The application of fuzzy rules to reliability estimation with practical implementations is innovative, making it possible to provide solutions to problems of reliable identification of gas turbines in their complex operating environments.