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Multi-sensor data fusion and deep learning-based prediction of excavator bucket fill rates
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-24 , DOI: 10.1016/j.autcon.2025.106008
Shijiang Li, Gongxi Zhou, Shaojie Wang, Xiaodong Jia, Liang Hou
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-24 , DOI: 10.1016/j.autcon.2025.106008
Shijiang Li, Gongxi Zhou, Shaojie Wang, Xiaodong Jia, Liang Hou
Accurately predicting the bucket fill rate of excavators is a challenging task due to factors such as material flowability and the complex coupling interactions between the material and the bucket. To address this challenge, this paper proposes a bucket fill rate prediction method based on multi-sensor data fusion and deep learning. The ITCBAM model was developed by integrating a Convolutional Block Attention Module (CBAM) into the InceptionTime framework, leveraging multi-source sensor data such as cylinder displacement and stereo vision to enable precise predictions of fill rates. Results show that the ITCBAM model achieves prediction errors of 9.48% and 10.65% on the familiar and unfamiliar test sets, respectively. Compared to physical models and other deep learning models, it demonstrates higher prediction accuracy and stronger generalization capability. This method facilitates excavation decision-making, enhances construction efficiency, and provides valuable insights for further research on the automation and real-time prediction of construction machinery.
中文翻译:
多传感器数据融合和基于深度学习的挖掘机铲斗填充率预测
准确预测挖掘机的铲斗填充率是一项具有挑战性的任务,因为材料流动性以及材料与铲斗之间复杂的耦合相互作用等因素。为了应对这一挑战,本文提出了一种基于多传感器数据融合和深度学习的桶填充率预测方法。ITCBAM 模型是通过将卷积块注意力模块 (CBAM) 集成到 InceptionTime 框架中而开发的,利用气缸排量和立体视觉等多源传感器数据来精确预测填充率。结果表明,ITCBAM 模型在熟悉和不熟悉的测试集上分别实现了 9.48% 和 10.65% 的预测误差。与物理模型和其他深度学习模型相比,它表现出更高的预测精度和更强的泛化能力。该方法有助于挖掘决策,提高施工效率,并为进一步研究工程机械的自动化和实时预测提供有价值的见解。
更新日期:2025-01-24
中文翻译:
多传感器数据融合和基于深度学习的挖掘机铲斗填充率预测
准确预测挖掘机的铲斗填充率是一项具有挑战性的任务,因为材料流动性以及材料与铲斗之间复杂的耦合相互作用等因素。为了应对这一挑战,本文提出了一种基于多传感器数据融合和深度学习的桶填充率预测方法。ITCBAM 模型是通过将卷积块注意力模块 (CBAM) 集成到 InceptionTime 框架中而开发的,利用气缸排量和立体视觉等多源传感器数据来精确预测填充率。结果表明,ITCBAM 模型在熟悉和不熟悉的测试集上分别实现了 9.48% 和 10.65% 的预测误差。与物理模型和其他深度学习模型相比,它表现出更高的预测精度和更强的泛化能力。该方法有助于挖掘决策,提高施工效率,并为进一步研究工程机械的自动化和实时预测提供有价值的见解。