A hybrid physics-based and data-driven method for gear contact fatigue life prediction

被引：15

作者：

Zhou, Changjiang ^{[1
]}

Wang, Haoye ^{[1
]}

Hou, Shengwen ^{[2
]}

Han, Yong ^{[3
]}

机构：

[1] Hunan Univ, State Key Lab Adv Design & Manufacture Vehicle Bod, Changsha 410082, Peoples R China

[2] Shaanxi Fast Gear Co Ltd, Shanxi Key Lab Gear Transmiss, Xian 710119, Peoples R China

[3] Xiamen Univ Technol, Sch Mech & Automot Engn, Xiamen 361024, Peoples R China

来源：

INTERNATIONAL JOURNAL OF FATIGUE | 2023年 / 175卷

基金：

中国国家自然科学基金;

关键词：

Gear contact fatigue; Life prediction; Deep learning; Small sample sets; REMAINING USEFUL LIFE; STRENGTH;

D O I：

10.1016/j.ijfatigue.2023.107763

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

A hybrid physics-based and data-driven method is proposed for gear contact fatigue life prediction. The parameters influencing the fatigue life are determined by the physics-based model. A deep belief network (DBN) model is developed to reveal the relationships between these parameters and fatigue life. A variational autoencoder (VAE) model is presented to expand the size of the training dataset. The proposed method is verified by a gear contact fatigue test, and the predictions are all within a factor of 1.5 scatter band of the experimental results. This work provides an effective method for life prediction with small sample sets.

引用

页数：9

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