Machine Reliability Optimization by Genetic Algorithm Approach

Machine Reliability Optimization by Genetic Algorithm Approach

Ngnassi Djami Aslain Brisco Dr

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Nzie Wolfgang

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Doka Yamigno Serge

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University of Ngaoundéré

Machine Reliability Optimization by Genetic Algorithm Approach

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Abstract

To define the reliability network of a system (machine), we start with a set of components arranged in an appropriate topology (series, parallel, or parallel-series), choose the best terms of the ratio performance / cost, and gather by links with the aim to combine them. This process requires a long time and effort, given the very large number of possible combinations, which becomes tedious for the analyst. For this reason, it is essential to use an appropriate optimization approach when designing any product. However, before trying to optimize, it is necessary to have a reliability assessment method. The objective of this paper is to display a meta-heuristic method, which is sustained on the genetic algorithm (GA) to improve the machines reliability. To achieve this objective, a methodology that consists of presenting the functionalities of genetic algorithms is developed. The result achieved is the proposal of a reliability network for the optimal solution.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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How to Cite This Article

Ngnassi Djami Aslain Brisco. 2020. \u201cMachine Reliability Optimization by Genetic Algorithm Approach\u201d. Global Journal of Research in Engineering - A : Mechanical & Mechanics GJRE-A Volume 20 (GJRE Volume 20 Issue A2).

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GJRE Volume 20 Issue A2

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Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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GJRE-A Classification FOR Code: 091399

Submission ReceivedDecember 15, 2019
Peer Review Double Blind
Handling Editor
Accepted January 2, 2020
Published January 15, 2020

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v1.2

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September 24, 2020

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