工业工程2024，Vol.27Issue(3)：64-77,86,15.DOI:10.3969/j.issn.1007-7375.240063
基于注意力机制堆叠LSTM的多传感器信息融合刀具磨损预测
Tool Wear Prediction Based on Multi-sensor Information Fusion Using Stacked LSTM with Attention Mechanism
摘要
Abstract
Tool wear is one of the critical factors affecting the quality and efficiency of computer numerical control(CNC)machine tools.To address the current issues of signal singularity and insufficient prediction accuracy in milling cutter wear predictions,a novel method for tool wear prediction is proposed,which involves the fusion of multi-sensor information based on a stacked long short-term memory(LSTM)network with attention mechanism.Initially multiple sensor signals are preprocessed,subsequently multi-domain features are extracted.These features are fused at the feature level using Kernel Principal Component Analysis(KPCA),providing the input for the subsequent network.A stacked LSTM network model with attention mechanism is used to enable adaptive learning of crucial information,achieving a predictive accuracy of 99.9%on the PHM2010 dataset.Comparative experiments are conducted with other algorithms and incorporating artificial noise to verify the high accuracy and robustness of the proposed model.关键词
刀具磨损/核主成分分析(KPCA)/信息融合/注意力机制/鲁棒性Key words
tool wear/kernel principal component analysis(KPCA)/information fusion/attention mechanism/robustness分类
管理科学引用本文复制引用
成佳闻,赛希亚拉图,张超勇,罗敏..基于注意力机制堆叠LSTM的多传感器信息融合刀具磨损预测[J].工业工程,2024,27(3):64-77,86,15.基金项目
中德重点研发资助项目(2023ZY01089) （2023ZY01089）
工信部高质量发展专项资助项目(2023ZY01089) （2023ZY01089）
