Xiong Yewei, Gao Xinwen, Ye Dahua
SHU-SUCG Research Centre of Building Information, Shanghai University, Shanghai 201400, China.
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2025 Mar 7;25(6):1650. doi: 10.3390/s25061650.
Disc cutters are essential for shield tunnel construction, and monitoring their wear is vital for safety and efficiency. Due to their position in the soil silo, it is more challenging to observe the wear of disc cutters directly, making accurate and efficient detection a technical challenge. However, existing methods that treat the problem as a classification task often overlook the issue of data imbalance. To solve these problems, this paper proposes an end-to-end detection method for disc cutter wear state called the Multivariate Selective Attention Prototype Network (MVSAPNet). The method introduces an attention prototype network for variable selection, which selects important features from many input parameters using a specialized variable selection network. To address the problem of imbalance in the wear data, a prototype network is used to learn the centers of the normal and wear state classes, and the detection of the wear state is achieved by detecting high-dimensional features and comparing their distances to the class centers. The method performs better on the data collected from the Ma Wan Cross-Sea Tunnel project in Shenzhen, China, with an accuracy of 0.9187 and an F1 score of 0.8978, yielding higher values than the experimental results of other classification models.
盘形滚刀是盾构隧道施工的关键部件，监测其磨损情况对施工安全和效率至关重要。由于盘形滚刀位于土仓内，直接观察其磨损情况更具挑战性，因此准确高效地检测磨损成为一项技术难题。然而，现有将该问题视为分类任务的方法往往忽略了数据不平衡问题。为解决这些问题，本文提出了一种用于盘形滚刀磨损状态的端到端检测方法，称为多变量选择性注意力原型网络（MVSAPNet）。该方法引入了用于变量选择的注意力原型网络，通过专门的变量选择网络从众多输入参数中选择重要特征。为解决磨损数据不平衡问题，使用原型网络学习正常状态和磨损状态类别的中心，通过检测高维特征并比较它们到类别中心的距离来实现磨损状态的检测。该方法在中国深圳妈湾跨海隧道项目收集的数据上表现更佳，准确率为0.9187，F1分数为0.8978，比其他分类模型的实验结果更高。
