Radial Basis Neural Network for Availability Analysis

Deepika Garg; Naresh Sharma

doi:10.15415/mjis.2019.72013

Authors

Deepika Garg School of Basic & Applied Sciences, GD Goenka University, Gurugram, 122 103 India
Naresh Sharma School of Basic & Applied Sciences, GD Goenka University, Gurugram, 122 103 India

DOI:

https://doi.org/10.15415/mjis.2019.72013

Keywords:

Availability prediction, Radial basis neural networks (RBNN)

Abstract

The appliance of radial basis neural network is demostrated in this paper. The method applies failure and repair rate signals to learn the hidden relationship presented into the input pattern. Statistics of availability of several years is considered and collected from the management of concern plant. This data is considered to train and calidate the radial basis neural network (RBNN). Subsequently validated RBNN is used to estimate the availability of concern plant. The main objective of using neural network approach is that it’s not require assumption, nor explicit coding of the problem and also not require the complete knowledge of interdependencies, only requirement is raw data of system functioning.

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