通过使用三轴振动数据的机器学习对CNC刀的故障诊断
Nagesh Tambake1, Bhagyesh Deshmukh1, Sujit Pardeshi2
1Department of Mechanical Engineering, Walchand Institute of Technology, Solapur, Maharashtra, India.
Heliyon
|February 3, 2025
概括
这项研究引入了一种机器学习方法,用于使用振动数据进行CNC吊床切割器故障诊断. 一个Ensemble模型实现了100%的准确性,用于实时预测性维护,提高了运营效率.
科学领域:
- 机械工程 机械工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 传统的CNC工具监控方法与实时,复杂的加工环境作斗争.
- 需要自动机器学习模型来提高工具可靠性和加工精度.
- 在CNC削刀的故障诊断对于运营效率至关重要.
研究的目的:
- 开发一种新的机器学习框架,用于精确诊断数控刀的故障.
- 为了利用三轴振动数据进行实时工具状况监测.
- 通过使用可解释人工智能来提高故障诊断系统的透明度和可靠性.
主要方法:
- 在健康和故障条件下从数控机收集振动信号的数据集.
- 从振动数据中提取统计特征 (RMS,峰值因子,Kurtosis).
- 训练和评估各种机器学习模型 (决策树,有效线性,神经网络,合并方法),包括可解释AI技术 (LIME,Shapley值).
主要成果:
- 集团模型在较低的计算成本下实现了完美的 (100%) 分类准确性.
- 可解释的人工智能技术提高了诊断系统的可解释性和可靠性.
- 拟议的框架在解决过度拟合,数据质量和模型可解释性方面表现优于现有的方法.
结论:
- 开发的机器学习框架为CNC加工中有效的预测性维护提供了基础.
- 集团模型的高精度和效率使其适合实时故障诊断.
- 未来的研究应该探索多传感器数据和转移学习,以获得更广泛的通用性和部署.
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