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使用多重线性回归来预测发动机油寿命.
Van Thai Nguyen1, Jan Furch2, Jan Koláček3
1Faculty of Military Technology, University of Defence, 65, Kounicova 65, Brno, 66210, Czech Republic.
Scientific reports
|September 29, 2025
概括
本研究使用FTIR光谱和多重线性回归来预测发动机油粘度. 开发的模型准确地估计了油的使用寿命,有助于对柴油发动机进行预测性维护.
科学领域:
- 三角学和滑科学 三角学和滑科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 发动机油粘度对于滑和性能至关重要.
- 预测发动机油降解对于维护和效率至关重要.
- 福里埃变换红外光谱 (FTIR) 为石油分析提供了一种非破坏性的方法.
研究的目的:
- 开发一个发动机油粘度在100°C时的预测模型.
- 使用FTIR光谱数据和关键油参数进行粘度预测.
- 估计柴油发动机机油的剩余使用寿命.
主要方法:
- 多重线性回归和贝叶斯模型平均值 (BMA) 用于模型开发.
- 从FTIR衍生的参数进行变量选择的逐步回归.
- 使用标准化实验室方法对使用过的机油样本 (n=221) 进行分析.
- 预处理FTIR光谱,包括基线校正,规范化和噪声过.
主要成果:
- 建立了一个强大的回归模型来预测100°C的粘度.
- 关键预测因素包括总基数 (TBN),燃料含量,氧化,硫化和抗磨损颗粒 (APP).
- 该模型实现了0.287的根平均平方误差 (RMSE),表明了高精度.
结论:
- 与回归建模相结合的FTIR光谱学提供了一种有效的方法来预测发动机油粘度.
- 开发的模型可以准确估计发动机油的使用寿命,支持预测性维护策略.
- 这种方法对于在恶劣条件下运行的柴油发动机尤其有价值.
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