智能制造中的机器人切削工具的数据驱动故障诊断方法
Adel Afia1, Fawzi Gougam2, Abdenour Soualhi3
1Department of Mechanical Engineering. & Productics, Faculty of Mechanical and Process Engineering, Houari Boumediene University of Science and Technology, Algeria; Department of Mechanical Engineering, Solid Mechanics and Systems Laboratory (LMSS), University Mhamed Bougara Boumerdes, Boumerdes, Algeria.
ISA transactions
|July 16, 2025
概括
本研究介绍了智能制造中工具状况监测 (TCM) 的混合方法,使用先进的信号处理和机器学习来检测工具损坏的准确度超过99%. 该方法通过准确分类工具状态来提高生产率和机器可靠性.
科学领域:
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 智能制造和工业4.0依赖工具状况监测 (TCM) 来提高生产率和机器正常运行时间.
- 通过TCM防止工具损坏对于自动化系统的运营效率和成本降低至关重要.
- 现有的TCM方法面临着来自异质传感器的杂,高维数据的挑战.
研究的目的:
- 开发一种强大的混合方法来监测机器人切削工具的状况.
- 准确检测,识别和分类四种不同的工具状态:健康,表面损伤,碎片损伤和断牙.
- 提高TCM系统的计算效率和分类准确性.
主要方法:
- 集成最大重叠离散波段包转换 (MODWPT) 与特征矩阵构建的健康指标.
- 树生长算法 (TGA) 的应用用于有效的特征选择,减少维度和增强分离.
- 使用高斯混合模型 (GMM) 来根据所选特征对工具状态进行分类.
主要成果:
- 实现了高分类精度:振动为99.04%,扭矩为95.51%,力信号为91.67%.
- 在未见的振动数据上证明了强大的通用性,测试准确率为98.44%.
- 与其他方法相比,TGA-GMM框架显示出优越的特征歧视和模型稳定性.
结论:
- 拟议的TGA-GMM混合框架是复杂制造环境中工具故障诊断的有效解决方案.
- 该方法平衡了计算效率和高分类准确性,这对于实时TCM应用至关重要.
- 这种方法有助于提高工业4.0中的机器人切削工具的可靠性和防止损坏.
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