Modeling failure rate for Fokker F-27 tires using neural network

Ahmed Z. Al-Garni; Saad A. Ahmed; Mohsin Siddiqui

Modeling failure rate for Fokker F-27 tires using neural network

Ahmed Z. Al-Garni^*, Saad A. Ahmed, Mohsin Siddiqui

^*Corresponding author for this work

King Fahd University of Petroleum & Minerals

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

An artificial neural network (ANN) model for predicting the failure rate of Fokker F-27 airplane tires utilizing the backpropagation algorithm as a learning rule is presented. A comparison of the neural model with the Weibull model is made for validation purposes. The results show that the failure rate predicted by the ANN is closer in agreement with the real data than the failure rate predicted by the Weibull model.

Original language	English
Pages (from-to)	29-37
Number of pages	9
Journal	Transactions of the Japan Society for Aeronautical and Space Sciences
Volume	41
Issue number	131
State	Published - May 1998

Keywords

Airplane Tires
Modeling
Neural Network
Weibull Model

ASJC Scopus subject areas

Aerospace Engineering
Space and Planetary Science

Cite this

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title = "Modeling failure rate for Fokker F-27 tires using neural network",

abstract = "An artificial neural network (ANN) model for predicting the failure rate of Fokker F-27 airplane tires utilizing the backpropagation algorithm as a learning rule is presented. A comparison of the neural model with the Weibull model is made for validation purposes. The results show that the failure rate predicted by the ANN is closer in agreement with the real data than the failure rate predicted by the Weibull model.",

keywords = "Airplane Tires, Modeling, Neural Network, Weibull Model",

author = "Al-Garni, \{Ahmed Z.\} and Ahmed, \{Saad A.\} and Mohsin Siddiqui",

year = "1998",

month = may,

language = "English",

volume = "41",

pages = "29--37",

journal = "Transactions of the Japan Society for Aeronautical and Space Sciences",

issn = "0549-3811",

number = "131",

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T1 - Modeling failure rate for Fokker F-27 tires using neural network

AU - Al-Garni, Ahmed Z.

AU - Ahmed, Saad A.

AU - Siddiqui, Mohsin

PY - 1998/5

Y1 - 1998/5

N2 - An artificial neural network (ANN) model for predicting the failure rate of Fokker F-27 airplane tires utilizing the backpropagation algorithm as a learning rule is presented. A comparison of the neural model with the Weibull model is made for validation purposes. The results show that the failure rate predicted by the ANN is closer in agreement with the real data than the failure rate predicted by the Weibull model.

AB - An artificial neural network (ANN) model for predicting the failure rate of Fokker F-27 airplane tires utilizing the backpropagation algorithm as a learning rule is presented. A comparison of the neural model with the Weibull model is made for validation purposes. The results show that the failure rate predicted by the ANN is closer in agreement with the real data than the failure rate predicted by the Weibull model.

KW - Airplane Tires

KW - Modeling

KW - Neural Network

KW - Weibull Model

UR - https://www.scopus.com/pages/publications/0032065796

M3 - Article

AN - SCOPUS:0032065796

SN - 0549-3811

VL - 41

SP - 29

EP - 37

JO - Transactions of the Japan Society for Aeronautical and Space Sciences

JF - Transactions of the Japan Society for Aeronautical and Space Sciences

IS - 131

ER -

Modeling failure rate for Fokker F-27 tires using neural network

Abstract

Keywords

ASJC Scopus subject areas

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