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使用实证和混合机器学习方法预测扭弦执行器的寿命

Hai Nguyen¹, Chanthol Eang¹, Seungjae Lee¹

¹Department of Computer Science and Engineering/Intelligent Robot Research Institute, Sun Moon University, Asan 31460, Republic of Korea.

Sensors (Basel, Switzerland)

|December 11, 2025

概括

预测扭弦执行器 (TSA) 的疲劳寿命对于机器人系统至关重要. 一种混合物理引导的机器学习模型显著提高了寿命预测的准确性,超过了传统方法.

科学领域:

机器人与机械工程机器人与机械工程
材料科学与工程材料科学与工程
计算科学计算科学

背景情况:

预测扭弦执行器 (TSA) 的疲劳寿命对于机器人系统的可靠性至关重要.
传统的经验方法往往无法捕捉疲劳行为的复杂非线性和随机因素.

研究的目的:

为了比较四个机器学习模型 (线性回归,随机森林,XGBoost,GPR) 预测TSA疲劳寿命的有效性.
开发和验证混合物理导向模型,将实证方程与机器学习集成在一起,以提高寿命预测.

主要方法:

训练并验证了四种不同的机器学习模型,使用来自144个TSA启动测试的实验数据.
开发了一种混合模型,将实证疲劳寿命方程与XGBoost残余校正方法相结合.
使用R平方,RMSE,MAE和交叉验证一致性评估模型性能.

主要成果:

混合物理导向模型在R² = 0.9856,RMSE = 5299.47和MAE = 3329.67周期中实现了卓越的性能.
实现了高的交叉验证一致性 (CV R² = 0.9752),证明了强度.
在预测准确性和概括性方面表现优于独立机器学习模型.

结论:

基于物理的机器学习提供了卓越的解释性和概括性,特别是在有限的实验数据下.
混合实证-ML模型显示了在可变条件下准确预测元件寿命的巨大潜力.
这种方法提高了灵活变速箱的机器人执行系统的可靠性预测.

关键词:

疲劳寿命预测疲劳寿命预测混合动力XGBoost 的情况.以物理为导向的机器学习.预测性维护是指预测性维护.可靠性建模的可靠性建模扭弦执行器 (TSA) 是一种扭弦执行器.不确定性量化不确定性量化

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