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多传感器异质信号融合变压器用于工具磨损预测
Ju Zhou1,2, Xinyu Liu3, Qianghua Liao1
1Tech X Academy, Shenzhen Polytechnic University, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究介绍了一种新的多传感器多域特征融合变压器 (MSMDT),用于准确预测工具磨损. 该模型有效地融合了异构的传感器数据,提高了制造业的预测准确性.
科学领域:
- 制造业 工程 制造工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 工具磨损监测对于制造效率和安全至关重要.
- 不同质的多源传感器信号对有效的数据融合提出了挑战.
- 现有的方法难以整合各种传感器数据,以准确预测磨损.
研究的目的:
- 提出一个新的多传感器多域特征融合变压器 (MSMDT) 模型.
- 通过克服多源传感器信号融合的挑战,实现精确的工具磨损预测.
- 加强对异质传感器数据的整合,以改善磨损特征分析.
主要方法:
- 开发了一个物理意识的特征提取框架,用于时间域,频域和波形包特征.
- 构建了一个统一的特征矩阵,以整合异质信号的互补特征.
- 采用无位置嵌入的变压器架构来实现自适应的跨域特征融合.
主要成果:
- 该MSMDT模型在工具磨损预测方面表现出卓越的性能.
- 实验结果验证了该模型在PHM2010数据集上的有效性.
- 拟议的方法在预测准确性方面超过了最先进的方法.
结论:
- 该MSMDT模型提供了一个有效的解决方案,用于工具磨损预测使用多源传感器数据.
- 创新的功能工程和跨模式的注意力机制是模型成功的关键.
- 该方法为整合异质信号在工业监测中提供了一个强大的框架.
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