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Updated: Jun 21, 2025

Surrogate Model Development for Digital Experiments in Welding
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Surrogate Model Development for Digital Experiments in Welding

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基于变压器和定制的门控制的多任务联合学习模型,用于预测工具的剩余使用寿命和健康状况

Chunming Hou1,2, Liaomo Zheng1,3

  • 1Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括

本研究引入了一种新的模型,用于同时预测工具剩余使用寿命 (RUL) 和磨损阶段. 变压器编码器和定制门控制 (TECGC) 模型实现了准确的预测,改善了工具维护策略.

关键词:
动态的 适应性的 适应性的多任务联合学习多任务联合学习剩余的使用寿命.变压器编码器编码器磨损阶段 磨损阶段 磨损阶段

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