Data-driven inference modeling based on an on-line Wang-Mendel fuzzy approach

Zhai, Yanwei; Lv, Zheng; Zhao, Jun; Wang, Wei; Leung, Henry

doi:10.1016/j.ins.2020.10.018

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Analyzing the Difficulty Of Detection1

Learning Algorithm Of the Anfis System1

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2021

2024

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Cited by 10 publications

(2 citation statements)

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“…Detection uses an assessment on a scale of 1 to 10. These steps are almost the same as references from (Zhai, Lv, Zhao, Wang, & Leung, 2021) and (Azad, 2018). The steps of this research begin by collecting review literature, identifying problems, and formulating the problem.…”

Section: Analyzing the Difficulty Of Detectionsupporting

confidence: 56%

Accident Analysis Work with Failure Mode and Effect Method Analysis at PT. Gansa Furindo Indonesia

et al. 2021

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Section: Analyzing the Difficulty Of Detectionsupporting

confidence: 56%

Accident Analysis Work with Failure Mode and Effect Method Analysis at PT. Gansa Furindo Indonesia

et al. 2021

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“…The learning algorithm starts by building an initial network, then an error back-propagation learning method is applied. Jang proposed a hybrid learning rule that combines a gradient descent algorithm with least squares estimation, which can be expressed as follows [30,31]:…”

Section: Learning Algorithm Of the Anfis Systemmentioning

confidence: 99%

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

Hadroug

Hafaifa

Iratni

et al. 2021

Fuzzy Information and Engineering

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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.

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