减少对基于振动的钻头状况监测的标签依赖,使用掩盖功能进行预训练
M N Chandan1,2, Avinash Badadhe2, Alemu Workie Kebede3
1Department of Mechanical Engineering, G H Raisoni College of Engineering and Management, Pune, Maharashtra, 412207, India.
Scientific reports
|January 28, 2026
概括
本研究介绍了一种用于工具状态监测 (TCM) 的自主监督掩盖特征训练 (SSL-MFP) 框架. 该方法在机器学习中显著减少了对标记数据的需求,使得使用更少的注释能够准确地进行磨损分类.
科学领域:
- 制造业 工程 制造工程
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 工具状态监测 (TCM) 对于精密加工至关重要,以防止不准确性和停机时间.
- 目前的数据驱动诊断通常需要广泛的标记数据集,这阻碍了工业采用.
- 由于数据限制,现有方法在适应新工具或加工条件方面面临挑战.
研究的目的:
- 为工具状态监测 (TCM) 开发一个数据效率高的框架.
- 减少对大型标记数据集的依赖,用于TCM中的机器学习模型.
- 为了实现精确的钻井磨损分类,最小的注释努力.
主要方法:
- 引入了一个自我监督的蒙面特征训练 (SSL-MFP) 框架.
- 通过重建掩盖的时间频率特征来学习潜伏振动表示.
- 微调一个预训练的编码器,使用一小部分标记数据进行分类.
主要成果:
- 实现了与完全监督模型可比的分类准确性.
- 证明了有效的概括,标记样本显著减少.
- 在学习特征多元体中展示了明显的类可分离性,证实了表示力的强度.
结论:
- SSL-MFP框架为TCM提供了一种数据效率高的方法.
- 这种方法解决了与标签成本和适应相关的关键工业挑战.
- 允许在工业环境中实际部署用于TCM的机器学习.
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