Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction

Yinze Yan; Zhengjie Tian; Shengwen Hou; Zhiqiang Cai

doi:10.1109/IEEM55944.2022.9989733

Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction

Yinze Yan, Zhengjie Tian, Shengwen Hou, Zhiqiang Cai

School of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

It is very important to analyze and predict the fatigue life of gear, which is the key part of transmission. Due to the small amount of bending fatigue life data, two sample expansion methods, intermediate interpolation method and Lagrange interpolation method, are used to expand the amount of data, establish equal spacing and non-equal spacing grey GM (1,1) prediction models respectively, and test the models. The results show that the most accurate prediction results can be obtained without interpolation for non-equal spacing models, while the most accurate prediction results can be obtained by Lagrange interpolation for non-equal spacing models. Compare the gray GM (1,1) prediction model with three traditional prediction methods, the results show that the gray GM(1,1) prediction model can obtain the most accurate prediction results for small data. It provides the manufacturer with the processing method of small data and the prediction method of gear bending fatigue life under unknown stress.

Original language	English
Title of host publication	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Publisher	IEEE Computer Society
Pages	492-496
Number of pages	5
ISBN (Electronic)	9781665486873
DOIs	https://doi.org/10.1109/IEEM55944.2022.9989733
State	Published - 2022
Event	2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 - Kuala Lumpur, Malaysia Duration: 7 Dec 2022 → 10 Dec 2022

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2022-December
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	7/12/22 → 10/12/22

Keywords

Bending fatigue life
Gear
Grey GM (1,1) prediction

Access to Document

10.1109/IEEM55944.2022.9989733

Cite this

Yan, Y., Tian, Z., Hou, S., & Cai, Z. (2022). Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction. In IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 (pp. 492-496). (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2022-December). IEEE Computer Society. https://doi.org/10.1109/IEEM55944.2022.9989733

@inproceedings{b42a7c9304a04bde90aaadb93ac75b9c,

title = "Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction",

abstract = "It is very important to analyze and predict the fatigue life of gear, which is the key part of transmission. Due to the small amount of bending fatigue life data, two sample expansion methods, intermediate interpolation method and Lagrange interpolation method, are used to expand the amount of data, establish equal spacing and non-equal spacing grey GM (1,1) prediction models respectively, and test the models. The results show that the most accurate prediction results can be obtained without interpolation for non-equal spacing models, while the most accurate prediction results can be obtained by Lagrange interpolation for non-equal spacing models. Compare the gray GM (1,1) prediction model with three traditional prediction methods, the results show that the gray GM(1,1) prediction model can obtain the most accurate prediction results for small data. It provides the manufacturer with the processing method of small data and the prediction method of gear bending fatigue life under unknown stress.",

keywords = "Bending fatigue life, Gear, Grey GM (1,1) prediction",

author = "Yinze Yan and Zhengjie Tian and Shengwen Hou and Zhiqiang Cai",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022 ; Conference date: 07-12-2022 Through 10-12-2022",

year = "2022",

doi = "10.1109/IEEM55944.2022.9989733",

language = "英语",

series = "IEEE International Conference on Industrial Engineering and Engineering Management",

publisher = "IEEE Computer Society",

pages = "492--496",

booktitle = "IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022",

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Yan, Y, Tian, Z, Hou, S & Cai, Z 2022, Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction. in IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022. IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2022-December, IEEE Computer Society, pp. 492-496, 2022 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022, Kuala Lumpur, Malaysia, 7/12/22. https://doi.org/10.1109/IEEM55944.2022.9989733

Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction. / Yan, Yinze; Tian, Zhengjie; Hou, Shengwen et al.
IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2022. IEEE Computer Society, 2022. p. 492-496 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2022-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction

AU - Yan, Yinze

AU - Tian, Zhengjie

AU - Hou, Shengwen

AU - Cai, Zhiqiang

PY - 2022

Y1 - 2022

N2 - It is very important to analyze and predict the fatigue life of gear, which is the key part of transmission. Due to the small amount of bending fatigue life data, two sample expansion methods, intermediate interpolation method and Lagrange interpolation method, are used to expand the amount of data, establish equal spacing and non-equal spacing grey GM (1,1) prediction models respectively, and test the models. The results show that the most accurate prediction results can be obtained without interpolation for non-equal spacing models, while the most accurate prediction results can be obtained by Lagrange interpolation for non-equal spacing models. Compare the gray GM (1,1) prediction model with three traditional prediction methods, the results show that the gray GM(1,1) prediction model can obtain the most accurate prediction results for small data. It provides the manufacturer with the processing method of small data and the prediction method of gear bending fatigue life under unknown stress.

AB - It is very important to analyze and predict the fatigue life of gear, which is the key part of transmission. Due to the small amount of bending fatigue life data, two sample expansion methods, intermediate interpolation method and Lagrange interpolation method, are used to expand the amount of data, establish equal spacing and non-equal spacing grey GM (1,1) prediction models respectively, and test the models. The results show that the most accurate prediction results can be obtained without interpolation for non-equal spacing models, while the most accurate prediction results can be obtained by Lagrange interpolation for non-equal spacing models. Compare the gray GM (1,1) prediction model with three traditional prediction methods, the results show that the gray GM(1,1) prediction model can obtain the most accurate prediction results for small data. It provides the manufacturer with the processing method of small data and the prediction method of gear bending fatigue life under unknown stress.

KW - Bending fatigue life

KW - Gear

KW - Grey GM (1,1) prediction

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Prediction of Gear Bending Fatigue Life Based on Grey GM (1,1) Prediction

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