Current Situation and Developing Trend of Fatigue Life Prediction of Components based on Data-driven

Xiuhua Zhang; Huaiju Liu; Caichao Zhu; Peitang Wei; Shaojie Wu

doi:10.16578/j.issn.1004.2539.2021.10.001

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Current Situation and Developing Trend of Fatigue Life Prediction of Components based on Data-driven

Review | 更新时间：2022-11-10

- Current Situation and Developing Trend of Fatigue Life Prediction of Components based on Data-driven
- Journal of Mechanical Transmission Vol. 45, Issue 10, Pages: 1-14(2021)
- 作者机构：
  
  1.重庆大学机械传动国家重点实验室，重庆 400030
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.10.001
  CLC：
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张秀华,刘怀举,朱才朝等.基于数据驱动的零部件疲劳寿命预测研究现状与发展趋势[J].机械传动,2021,45(10):1-14.

Zhang Xiuhua,Liu Huaiju,Zhu Caichao,et al.Current Situation and Developing Trend of Fatigue Life Prediction of Components based on Data-driven[J].Journal of Mechanical Transmission,2021,45(10):1-14.
张秀华,刘怀举,朱才朝等.基于数据驱动的零部件疲劳寿命预测研究现状与发展趋势[J].机械传动,2021,45(10):1-14. DOI： 10.16578/j.issn.1004.2539.2021.10.001.

Zhang Xiuhua,Liu Huaiju,Zhu Caichao,et al.Current Situation and Developing Trend of Fatigue Life Prediction of Components based on Data-driven[J].Journal of Mechanical Transmission,2021,45(10):1-14. DOI： 10.16578/j.issn.1004.2539.2021.10.001.

摘要

随着风电、高铁、航空等重大装备向着高可靠性、长寿命、智能化的方向发展，对齿轮、轴承等基础零部件的寿命提出更高的要求，也迫切需要更为科学、高效的疲劳寿命预测方法。机械零部件的寿命预测方法可分为基于物理失效模型、基于数据驱动模型和基于融合模型3种。随着零部件寿命预测研究向高精度、高效率发展，基于物理模型的寿命预测方法由于其模型复杂、耗时、不具有普适性等缺陷难以满足现代需求。基于数据驱动技术由于其具有无需知道其具体失效机理、预测结果准确等优点，且伴随机器学习、深度学习等技术的迅速发展，使得其成为零部件疲劳寿命预测研究的热点。鉴于此，详细阐述了基于数据驱动的机械零部件疲劳寿命预测方法，并详细介绍了神经网络、支持向量机、随机森林、深度学习等数据驱动方法在零部件寿命预测中的应用，总结了每种方法的特点，探讨了基于数据驱动的零部件寿命预测方法的发展趋势，并给出了基于GA-BP神经网络齿轮接触疲劳寿命预测研究的案例。

Abstract

With the development of wind turbine，high-speed railway，aero-engine and other large equipment towards the direction of high reliability，long life and intelligence，it has put forward higher requirements for the life of basic components such as gears and bearings. It is urgent to use more scientific and efficient fatigue life prediction method. The life prediction methods of mechanical components can be divided into physical failure model，data-driven model and fusion model（physical failure and data-driven model fusion） three types. With the development of components life prediction research towards high precision and high efficiency，physical model is difficult to meet modern needs due to its complexity，time-consuming and non-universal disadvantages. With the rapid development of machine learning，deep learning and other technologies，data-driven model has become a hot topic in the research of components fatigue life prediction of due to the advantages of no need to know detailed failure mechanism and accurate prediction results. In view of this，the fatigue life prediction method of components based on data-driven is described. The application of these methods in the life prediction of components is introduced，including neural network，support vector machine，random forest and deep learning，and the characteristics of each method are summarized，and the developing trend of the life prediction method of components based on data-driven is discussed. And a case study of gear contact fatigue life prediction based on GA-BP neural network is presented.

关键词

数据驱动零部件寿命预测机器学习预测精度

Keywords

Data-drivenComponentsLife predictionMachine learningPrediction accuracy

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