使用各种转移学习方法对一般化冲压机故障诊断模型进行比较和优化
Po-Wen Hwang1, Yuan-Jen Chang1,2, Hsieh-Chih Tsai2
1Department of Aerospace and Systems Engineering, Feng Chia University, Taichung City 407102, Taiwan.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
一个新的通用人工智能 (AI) 模型使用振动数据准确预测印机故障. 这种人工智能方法可以在各种冲压设备中进行预测性维护,提高制造质量和效率.
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:
- 冲压操作需要精确的总清除,以确保设备的质量和寿命.
- 不同的冲压机设计阻碍了通用故障诊断模型的开发.
- 实时监控总清除对于过程控制和故障检测至关重要.
研究的目的:
- 为印机开发一个通用的故障诊断模型.
- 为了在不同机器类型中实现有效的过程控制和预测性维护.
- 为了克服智能制造中机器特定模型开发的挑战.
主要方法:
- 使用了来自四种不同的冲压机型 (OCP-110,G2-110,G2-160,ST1-110) 的加速度计的振动数据.
- 评估了四个深度学习架构:CNN,CNN-Res,VGG16和ResNet50,并提供了微调策略.
- 开发了一种通用故障诊断模型,适用于多种冲压机类型.
主要成果:
- 一般化故障诊断模型实现了平均准确性,回忆和F1得分超过99%.
- 在现实世界印故障诊断中证明了高效率和可靠性.
- 在OCP-110,G2-110,G2-160和ST1-110机器模型中验证了该模型的性能.
结论:
- 开发的通用模型有效地诊断各种冲压机的故障.
- 这种人工智能驱动的方法简化了智能制造中的预测性维护部署.
- 该模型显示了可扩展到更多机器类型和操作条件的潜力.
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