在细分的可变加工条件下,在考虑降解指标的合效应的情况下,对削工具的剩余使用寿命预测
Tingting Feng1, Liang Guo1, Naipeng Li2
1School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
ISA transactions
|March 9, 2026
概括
本研究引入了一种新方法,用于在变化的加工条件下预测削工具剩余使用寿命 (RUL). 它使用自适应性降解指标和二进制维纳过程来提高RUL预测的准确性.
科学领域:
- 机械工程 机械工程
- 制造业科学 制造业科学
- 预测性维护是指预测性维护.
背景情况:
- 在多品种,小批量生产中,加工参数经常发生切换,导致指示器跳转.
- 现有的剩余使用寿命 (RUL) 预测方法与细分参数和单一指标作斗争.
- 不准确的RUL预测阻碍了高效的工具管理和生产规划.
研究的目的:
- 为分段可变加工条件开发精确的削工具RUL预测方法.
- 为应对参数切换和降解指标不足所带来的挑战.
- 提高灵活制造中的预测性维护的可靠性.
主要方法:
- 从多源传感信号中提取了两个特征性降解指标.
- 应用了基线转换算法来消除参数切换效应.
- 开发了一种具有随机效应的非线性二进制维纳过程,以建模指标相关性.
- 利用Copula函数来建模边际RUL分布之间的依赖性,用于合预测.
主要成果:
- 拟议的方法有效地预测在细分变量条件下削工具的RUL.
- 条件适应性降解指标为工具健康提供了更全面的视图.
- 二元维纳过程准确地捕获了降解指标之间的相关性.
- 实验验证证证实了该方法的有效性和稳定性.
结论:
- 这种新的方法可以在复杂的加工环境中提高RUL预测的准确性.
- 条件适应性指标和合的RUL预测增强了预测性维护能力.
- 这种方法为灵活制造中的工具寿命管理提供了更可靠的解决方案.
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