一个混合CBiGRUPE模型用于准确的磨轮磨损预测
Sumei Si1, Deqiang Mu1, Hailiang Tang1
1School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种混合CBiGRUPE模型,用于预测研磨轮磨损,提高精度和降低加工成本. 该模型在现有磨损监测方法上表现出优越的性能.
科学领域:
- 制造业 工程 制造工程
- 人工智能在制造业中的应用
- 机器状态监测 机器状态监测
背景情况:
- 有效监测研磨轮磨损对于保持加工质量和研磨操作的经济效率至关重要.
- 现有的磨损预测方法往往在准确性和稳定性方面面临限制.
研究的目的:
- 开发和验证一种新的混合模型,CBiGRUPE,用于准确预测研磨轮磨损.
- 将拟议模型的性能与CNN,BiGRU和Transformer等既有方法进行比较.
主要方法:
- 从表面研磨机的螺旋电机电流信号中提取时间域特征.
- 将卷积神经网络 (CNN),双向门循环单元 (BiGRU) 和执行编码器集成到CBiGRUPE模型中.
- 使用贝叶斯优化优化模型结构和超参数的优化.
主要成果:
- 该CBiGRUPE模型取得了优异的表现,MAE为3.041,RMSE为3.927和R2为0.920.
- 与CNN,BiGRU和变压器模型相比,在磨损预测方面表现出卓越的准确性和稳定性.
- 实验验证证了拟议的混合方法的有效性.
结论:
- CBiGRUPE模型为预测研磨轮磨损提供了一个高度准确和稳定的解决方案.
- 这项研究为实施轮胎磨损补偿和优化穿衣策略提供了基础.
- 该研究强调了混合深度学习模型在先进制造监控中的潜力.
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