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216105

SURVEY OF GEAR FAULT DIAGNOSIS USING VARIOUS STATISTICAL SIGNALS PARAMETERS

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Last updated: 25 Dec 2024

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Abstract

Gears are critical components of industrial equipment, where gear failure results machinery failure and that consider as a significant reduction in productivity. It is always critical to keep track of the machine's health in time. Consequently, researchers have been working on developing methods for identifying and diagnosing gear problems. The purpose of this paper is focused to provide a review of a variety of diagnosis techniques that have been shown to be successful when applied to rotating machinery such as gears, as well as to highlight fault detection and identification techniques that are primarily based on vibration analysis. fluctuations from these standards generate distinctive vibration signals whose help in monitoring the gearbox malfunctions. The main sources of these fluctuations are crack tooth, chipped tooth, missing tooth, the surface wear during heat treatment or gearbox assembly, and the geometrical errors, resulting from the gear cutting process and wear. In conclusions, a brief explanation of a novel method of diagnosis based on hybrid artificial intelligence approaches that incorporate neural networks, fuzzy sets, expert systems, and fault detection is provided.

DOI

10.21608/jest.2022.216105

Keywords

condition monitoring, gearbox, Vibration analysis, gear fault, time domain analysis, frequency domain analysis

Authors

First Name

D. Y.

Last Name

Samuel

MiddleName

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Affiliation

Mechatronics Program, Faculty of Engineering, Minia University, El-Minia 61111, EGYPT,

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Orcid

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First Name

A.

Last Name

Nabhan

MiddleName

-

Affiliation

Production Engineering and Mechanical Design, Faculty of Engineering, Minia University, El-Minia 61111, EGYPT.

Email

-

City

-

Orcid

-

First Name

M. O.

Last Name

Mousa

MiddleName

-

Affiliation

Production Engineering and Mechanical Design, Faculty of Engineering, Minia University, El-Minia 61111, EGYPT.

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-

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Orcid

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Volume

19

Article Issue

1

Related Issue

30427

Issue Date

2022-01-01

Receive Date

2022-09-12

Publish Date

2022-01-01

Page Start

14

Page End

27

Print ISSN

2090-5882

Online ISSN

2090-5955

Article File

JEST_Volume 19_Issue 1_Pages 14-27.pdf

PDF . 484.3kB

Link

https://jest.journals.ekb.eg/article_216105.html

Detail API

https://jest.journals.ekb.eg/service?article_code=216105

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2

Type

Original Article

Type Code

1,211

Publication Type

Journal

Publication Title

Journal of the Egyptian Society of Tribology

Publication Link

https://jest.journals.ekb.eg/

MainTitle

SURVEY OF GEAR FAULT DIAGNOSIS USING VARIOUS STATISTICAL SIGNALS PARAMETERS

Details

Type

Article

Created At

22 Jan 2023