Recurrent neural networks for real-time prediction of TBM operating parameters
Recurrent neural networks for real-time prediction of TBM operating parameters
复制标题
用于实时预测 TBM 操作参数的循环神经网络
DOI：
10.1016/j.autcon.2018.11.013
复制
发表时间：
2019-02-01
影响因子：
10.3
通讯作者：
Zhang, Hongwei
中科院分区：
文献类型：
--
作者：
Gao, Xianjie;Shi, Maolin;Zhang, Hongwei
随着隧道掘进机在隧道施工中的广泛应用，掘进机运行状态的适应性调整成为研究热点。掘进前对隧道地质条件的预测仍然是一个难题，因此对TBM重要运行参数的预测在TBM适应性调整研究中具有重要作用。本文利用传统回归神经网络、长短期记忆（LSTM）网络和门控回归单元（GRU）网络等三种回归神经网络（RNN），根据TBM现场运行数据进行TBM运行参数的实时预测。实验结果表明，所提出的3种基于RNN的预测器能够准确预测下一阶段的TBM运行参数，包括扭矩、速度、推力和膛压。我们还与几个经典的回归模型进行了比较（例如，支持向量回归（SVR）、随机森林（RF）和Lasso），但它们实际上不能作为真实的实时预测器，对比实验表明，提出的基于RNN的预测器在大多数情况下优于回归模型。将RNN用于TBM运行参数实时预测的可行性表明，RNN能够对从各种施工设备上采集的时间连续的现场数据进行分析和预测。
With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavation, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. In this paper, we use three kinds of recurrent neural networks (RNNs), including traditional RNNs, long-short term memory (LSTM) networks and gated recurrent unit (GRU) networks, to deal with the real-time prediction of TBM operating parameters based on TBM in-situ operating data. The experimental results show that the proposed three kinds of RNN-based predictors can provide accurate prediction values of some important TBM operating parameters during next period including the torque, the velocity, the thrust and the chamber pressure. We also make a comparison with several classical regression models (e.g., support vector regression (SVR), random forest (RF) and Lasso) which actually cannot act as real-time predictors in a real sense, and the comparative experiments show that the proposed RNN-based predictors outperform the regression models in most cases. The feasibility of RNNs for the real-time prediction of TBM operating parameters indicates that RNNs can afford the analysis and the forecasting of the time-continuous in-situ data collected from various construction equipments.