在移动机械接触中预测磨损:回归算法和特征选择技术的比较分析
Jianjie Jiang1, Intisar Omar1, Muhammad Khan1
1School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK.
ISA transactions
|August 26, 2025
概括
使用机器学习模型准确预测对于工业应用至关重要. 这项研究开发了一种用于磨损体积估计的预测模型,以优化特征选择和样本大小以提高准确性.
科学领域:
- 材料科学
- 机械工程
- 数据科学
背景情况:
- 精确的磨损预测对于降低运行风险,尽量减少停机时间和延长制造,运输和发电中的组件寿命至关重要.
- 在受控条件下,pin-on-disc系统被广泛用于研究三角学行为和材料磨损.
研究的目的:
- 开发和评估基于机器学习的预测模型,用于估计针盘系统的磨损量.
- 确定影响磨损的最有影响的参数,并比较不同回归算法的性能.
主要方法:
- 使用实验数据,包括摩擦系数,接触力,透深度,滑动距离,声压和负载.
- 使用特征选择技术 (包装和嵌入) 来确定关键预测参数.
- 确定最佳样本大小以平衡模型的复杂性并防止过度拟合.
- 应用和比较的回归模型:线性回归,支向量机 (SVM) 和随机森林 (RF).
- 使用平均绝对误差 (MAE),平均偏差误差 (MBE),根平均平方误差 (RMSE) 和确定系数 (R2) 评估模型性能.
主要成果:
- 功能选择成功地确定了对磨损预测最相关的参数,提高了模型的准确性.
- 优化样本大小提高了模型的概括性,减轻了不合适和过度合适.
- 比较分析表明不同回归算法对磨损量估计的预测能力有所不同.
- 开发的模型提供了经过定义的错误指标的磨损体积的定量评估.
结论:
- 这项研究提出了一种系统的机器学习方法,用于准确地预测pin-on-disc系统的磨损量.
- 这些发现为分析磨损的参数重要性和算法性能提供了宝贵的见解.
- 这项研究为实践者和研究人员提供了强大的框架,旨在提高磨损预测的准确性并降低工业运营成本.
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