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未标记数据增强工具基于图形神经网络的仍然有用的生命预测

Dingli Guo¹, Honggen Zhou¹, Li Sun¹

¹School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China.

Sensors (Basel, Switzerland)

|July 12, 2025

概括

本研究介绍了一种未标记数据增强的方法,用于切割工具的剩余使用寿命 (RUL) 预测. 它利用丰富的未标记数据和图形神经网络来提高RUL预测的准确性和概括性.

科学领域:

制造业工程制造工程
人工智能的人工智能
机器学习机器学习

背景情况:

准确预测切割工具的剩余使用寿命 (RUL) 对于高效的制造,防止停机时间和成本至关重要.
目前用于RUL预测的深度学习方法受到有限的标记数据的阻碍,这些数据的获取是昂贵和耗时的.
大量的未标记的加工数据在实际应用中往往未得到充分利用.

研究的目的:

通过有效利用丰富的未标记数据,提出一种提高工具RUL预测的新方法.
为了解决RUL预测的监督学习数据稀缺性的局限性.
提高RUL预测模型的准确性和概括能力.

主要方法:

开发了一个自定义的标准和损失函数,用于在未标记的数据上训练模型,并结合了工具磨损进展的物理规则.
员工转移学习将从未标记的数据中学到的知识转移到在标记的数据上训练的模型中.
利用图形神经网络 (GNN) 进行多传感器数据融合,以提取更丰富的信息.

主要成果:

拟议的方法有效地利用未标记的数据来增强工具RUL预测.
转移学习成功地将未标记数据的知识集成到RUL预测模型中.
基于GNN的多传感器融合提高了未标记数据增强的有效性.

结论:

无标签数据增强方法显著提高了切割工具RUL预测模型的准确性和概括性.
这种方法为在工业环境中利用未充分利用的未标记数据提供了可行的解决方案.
集成的GNN进一步提高了性能,使复杂的数据融合.

关键词:

图表神经网络的神经网络多传感器数据融合数据工具 RUL 预测工具转移学习学习转移学习没有标记的数据增强.

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