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A real-time multi-head mixed attention mechanism-based prediction method for tunnel boring machine disc cutter wear

Science China Technological Sciences 2025年第1期 290-311页

作者： Haodi WANG Chengjin QIN honggan yu Zhinan ZHANG Guoqiang HUANG Zhengyang LIU Chengliang LIU State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong University

In the process of hard rock tunnel excavation, workers often need to enter the tunnel boring machine(TBM)cutterhead at regular intervals to measure cutter wear. However, this method is time-consuming and labor-intensive. Existing cutter prediction models primarily rely on geological parameters to predict overall cutter wear before construction, making it challenging to monitor real-time wear at different locations of the cutter and obtain accurate geological parameters. To address these challenges, this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter. First, a method of cutter wear normalization to eliminate measurement noise is explored. Then, considering the complex correlation of TBM operating parameters in feature and time dimensions, a new multi-head mixed attention mechanism model is designed to establish the dependency between different features and different moments, to better establish the mapping model between operating parameters and cutter wear. Finally, the current cutter wear state can be calculated by accumulating the wear amount of all previous small excavation sections. The effectiveness of the method is verified by using field data from the Mumbai tunnel. The results demonstrate that the method is capable of real-time prediction of front cutter and edge cutter wear on the test set, achieving an average accuracy rate of 95.75%. Moreover, the method can update the cutter wear status after every meter of excavation, which has good real-time performance. In addition, the average accuracy of cutter wear prediction of the proposed method is 11.75%, 10.375%, 3.875%, 3.625%, 1.375%, 3.125%, 2.25%, and 0.75%higher than that of LSTM, CNN, LSTM-CNN, CACNN, SACNN, MMADNN, MTACNN, and MFACNN. In summary, this approach offers an accurate prediction of cutter wear state while reducing inspection time and costs and has high application value.

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Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction

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Geoscience Frontiers 2023年第2期14卷 86-104页

作者： Chengjin Qin Guoqiang Huang honggan yu Ruihong Wu Jianfeng Tao Chengliang Liu State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Due to the closed working environment of shield machines,the construction personnel cannot observe the construction geological environment,which seriously restricts the safety and efficiency of the tunneling *** this study,we present an enhanced multi-head self-attention convolution neural network(EMSACNN)with two-stage feature extraction for geological condition prediction of shield ***,we select 30 important parameters according to statistical analysis method and the working principle of the shield ***,we delete the non-working sample data,and combine 10 consecutive data as the input of the ***,to deeply mine and extract essential and relevant features,we build a novel model combined with the particularity of the geological type recognition task,in which an enhanced multi-head self-attention block is utilized as the first feature extractor to fully extract the correlation of geological information of adjacent working face of tunnel,and two-dimensional CNN(2dCNN)is utilized as the second feature *** performance and superiority of proposed EMSACNN are verified by the actual data collected by the shield machine used in the construction of a double-track tunnel in Guangzhou,*** results show that EMSACNN achieves at least 96%accuracy on the test sets of the two tunnels,and all the evaluation indicators of EMSACNN are much better than those of classical AI model and the model that use only the second-stage feature ***,the proposed EMSACNN achieves high accuracy and strong generalization for geological information prediction of shield machine,which is of great guiding significance to engineering practice.

关键词： Geological information prediction Shield machine Enhanced multi-head self-attention CNN

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Anti‐noise diesel engine misfire diagnosis using a multi‐scale CNN‐LSTM neural network with denoising module

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 963-986页

作者： Chengjin Qin Yanrui Jin Zhinan Zhang honggan yu Jianfeng Tao Hao Sun Chengliang Liu State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiChina

Currently,accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited.A novel multi‐scale CNN‐LSTM neural network(MSCNN‐LSTMNet)is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire ***,a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is *** the essential characteristics of measured vibration signals at different scales,a multi‐scale convolutional NN(CNN)block is designed to realize multi‐scale feature ***,multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features,enhancing the robustness of the *** this basis,the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisa-tion *** effectiveness of MSCNN‐LSTMNet is validated by experi-mental results of both one‐and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working *** results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing *** strong noise conditions(−10 dB signal‐to‐noise ratio)for four datasets,MSCNN‐LSTMNet obtained 97.561%average accuracy,while average accuracy for random forest,deep neural network,CNN and MSCNNNet were 73.828%,79.544%,82.247%,and 89.741%,***,for 11 noise generalisation tasks between different noise levels,MSCNN‐LSTMNet obtained at least 96.679%,97.849%,98.892%,and 94.010%accuracy on the four datasets,which are much higher than those of the existing methods.

关键词： fault diagnosis machine learning neural network

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A real-time multi-head mixed attention mechanism-based prediction method for tunnel boring machine disc cutter wear

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Science China Technological Sciences 2024年

In the process of hard rock tunnel excavation, workers often need to enter the tunnel boring machine (TBM)cutterhead at regular intervals to measure cutter wear. However, this method is time-consuming and labor-intensive. Existing cutter prediction models primarily rely on geological parameters to predict overall cutter wear before construction, making it challenging to monitor real-time wear at different locations of the cutter and obtain accurate geological parameters. To address these challenges, this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter. First, a method of cutter wear normalization to eliminate measurement noise is explored. Then, considering the complex correlation of TBM operating parameters in feature and time dimensions, a new multi-head mixed attention mechanism model is designed to establish the dependency between different features and different moments, to better establish the mapping model between operating parameters and cutter wear. Finally, the current cutter wear state can be calculated by accumulating the wear amount of all previous small excavation sections. The effectiveness of the method is verified by using field data from the Mumbai tunnel. The results demonstrate that the method is capable of real-time prediction of front cutter and edge cutter wear on the test set, achieving an average accuracy rate of 95.75%. Moreover, the method can update the cutter wear status after every meter of excavation, which has good real-time performance. In addition, the average accuracy of cutter wear prediction of the proposed method is 11.75%, 10.375%, 3.875%, 3.625%, 1.375%, 3.125%, 2.25%, and 0.75%higher than that of LSTM, CNN, LSTM-CNN, CACNN, SACNN, MMADNN, MTACNN, and MFACNN. In summary, this approach offers an accurate prediction of cutter wear state while reducing inspection time and costs and has high application value.

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基于谱分析和裂纹扩展方法的舱口角隅疲劳寿命预报方法

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上海交通大学学报 2019年第2期53卷 153-160页

作者：余宏淦黄小平张永矿上海交通大学高新船舶与深海开发装备协同创新中心上海200240

以某超大型集装箱船驾驶室前椭圆形角隅为例,采用谱分析结合裂纹扩展方法预报角隅疲劳寿命.首先通过水动力和结构响应分析得到全船响应,获得角隅在不同工况下的应力分布,并依此选定可能的起裂位置.然后将全船分析结果作为ANSYS中建立的... 详细信息

以某超大型集装箱船驾驶室前椭圆形角隅为例,采用谱分析结合裂纹扩展方法预报角隅疲劳寿命.首先通过水动力和结构响应分析得到全船响应,获得角隅在不同工况下的应力分布,并依此选定可能的起裂位置.然后将全船分析结果作为ANSYS中建立的带裂纹角隅模型二次分析的边界条件,求解应力强度因子并采用最大周向应力准则(MCSC)得到角隅在不同工况下的裂纹扩展路径,在大量计算的基础上回归出相应的角隅裂纹应力强度因子经验公式.最后依据ABS船级社推荐的谱分析法构建疲劳载荷谱,采用单一曲线模型和所获得角隅裂纹应力强度因子经验公式预报角隅疲劳寿命,为角隅疲劳寿命的校核提供参考.

关键词：舱口角隅裂纹扩展路径谱分析断裂力学疲劳寿命

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Bayesian optimization-enhanced ensemble learning for the uniaxial compressive strength prediction of natural rock and its application

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Geohazard Mechanics 2024年第3期2卷 197-215页

作者： Chukwuemeka Daniel Xin Yin Xing Huang Jamiu Ajibola Busari Amos Izuchukwu Daniel honggan yu yucong Pan Department of Civil Engineering Pan African University Institute for Basic SciencesTechnology and Innovation(PAUSTI)Jomo Kenyatta University of Agriculture and TechnologyNairobi P.O.Box 62000-00200Kenya Department of Architecture and Civil Engineering City University of Hong KongTat Chee AvenueKowloonHong KongChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China Department of Civil Engineering University of IlorinIlorinNigeria Department of Electrical and Electronics Engineering University of IlorinIlorinNigeria School of Civil Engineering Wuhan UniversityWuhan 430072China

Engineering disasters, such as rockburst and collapse, are closely related to structural instability caused byinsufﬁcient bearing capacity of geological materials. Uniaxial compressive strength (UCS) holds considerablesigniﬁcance in rock engineering projects. Consequently, this study endeavors to devise efﬁcient models for theexpeditious and economical estimation of UCS. Using a dataset of 729 samples, including the Schmidt hammerrebound number, P-wave velocity, and point load index data, we evaluated six algorithms, namely AdaptiveBoosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), LightGradient Boosting Machine (LightGBM), Random Forest (RF), and Extra Trees (ET) and utilized Bayesian Optimization (BO) to optimize the aforementioned algorithms. Moreover, we applied model evaluation metrics suchas Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Variance Accounted For (VAF), Nash-SutcliffeEfﬁciency (NSE), Weighted Mean Absolute Percentage Error (WMAPE), Coefﬁcient of Correlation (R), and Coefﬁcient of Determination (R2). Among the six models, BO-ET emerged as the most optimal performer duringtraining (RMSE ¼ 4.5042, MAE ¼ 3.2328, VAF ¼ 0.9898, NSE ¼ 0.9898, WMAPE ¼ 0.0538, R ¼ 0.9955, R2 ¼0.9898) and testing (RMSE ¼ 4.8234, MAE ¼ 3.9737, VAF ¼ 0.9881, NSE ¼ 0.9875, WMAPE ¼ 0.2515, R ¼0.9940, R2 ¼ 0.9875) phases. Additionally, we conducted a systematic comparison between ensemble andtraditional single machine learning models such as decision tree, support vector machine, and K-NearestNeighbors, thus highlighting the advantages of ensemble learning. Furthermore, the enhancement effect of BO ongeneralization performance was assessed. Finally, a BO-ET-based Graphical User Interface (GUI) system wasdeveloped and validated in a Tunnel Boring Machine-excavated tunnel.

关键词： Rock mechanics Uniaxial compressive strength Prediction model Ensemble learning Bayesian optimization

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基于已有数据和粒子滤波的Paris参数估计和剩余寿命预测

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中国造船 2021年第2期62卷 33-45页

作者：祝志远黄小平余宏淦孙力上海交通大学海洋工程国家重点实验室上海200240 上海交通大学机械与动力工程学院上海200240

论文对Paris模型及其材料参数C和m以及粒子滤波的基本原理进行了介绍,基于粒子滤波算法,对30Cr2Ni4MoV钢和2024-T42铝合金这两种材料的疲劳裂纹扩展试验数据进行了计算分析。计算过程中,C和m由初始分布逐渐向真实值靠拢,降低了材料参数... 详细信息

论文对Paris模型及其材料参数C和m以及粒子滤波的基本原理进行了介绍,基于粒子滤波算法,对30Cr2Ni4MoV钢和2024-T42铝合金这两种材料的疲劳裂纹扩展试验数据进行了计算分析。计算过程中,C和m由初始分布逐渐向真实值靠拢,降低了材料参数的不确定性,同时还得到了C和m的线性相关方程。预测得到了剩余寿命的中值和置信区间,预测结果和试验结果非常接近,验证了粒子滤波应用于疲劳裂纹扩展率参数估计和寿命预报的可行性和有效性。该方法可以用于疲劳裂缝扩展数据的处理和在役结构的剩余寿命预测。

关键词：粒子滤波疲劳裂纹扩展材料参数剩余寿命置信区间

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采用Kriging模型的开关磁阻电机开通角与关断角寻优方法

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西安交通大学学报 2020年第10期54卷 94-99,107页

作者：毛帅陶建峰余宏淦刘成良上海交通大学机械与动力工程学院上海200240 上海交通大学机械系统与振动国家重点实验室上海200240

针对传统密集采样方法获得采用角度位置控制的开关磁阻电机最优开通角与关断角所需实验次数多、耗时长,效率低的问题,提出一种采用Kriging模型的最优开通角和关断角实验求取方法来减少实验采样次数,提高寻优效率。首先,通过少量的初始... 详细信息

针对传统密集采样方法获得采用角度位置控制的开关磁阻电机最优开通角与关断角所需实验次数多、耗时长,效率低的问题,提出一种采用Kriging模型的最优开通角和关断角实验求取方法来减少实验采样次数,提高寻优效率。首先,通过少量的初始样本点建立开通角和关断角与电机振动加速度之间的预测模型;之后,根据得到的预测模型有针对性地在最优开通角和关断角可能出现的区域进行采样计算;最后,当区域缩小到满足精度要求的大小后,对该区域进行密集采样,从而快速准确发现最优开通角和关断角。实验结果表明,基于Kriging预测模型获取相同开关磁阻电机最优开通角和关断角(误差小于1%以内)所需样本数仅为传统的密集采样方法的12.4%,计算时间缩短了62.8%。因此,所提方法能显著提高开关磁阻电机的开通角和关断角寻优实验效率。

关键词：开关磁阻电机 Kriging模型开通角关断角角度位置控制

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基于随机森林与粒子群算法的隧道掘进机操作参数地质类型自适应决策

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中南大学学报（自然科学版） 2023年第4期54卷 1311-1324页

作者：刘明阳陶建峰覃程锦余宏淦刘成良上海交通大学机械与动力工程学院上海200240

考虑到隧道掘进机的性能对地质条件比较敏感且其操作依赖于司机经验,提出基于随机森林和粒子群算法的隧道掘进机操作参数地质条件自适应决策方法。利用随机森林(RF)分别建立地质类型、操作参数与推进速度、刀盘转矩的映射关系模型;结合... 详细信息

考虑到隧道掘进机的性能对地质条件比较敏感且其操作依赖于司机经验,提出基于随机森林和粒子群算法的隧道掘进机操作参数地质条件自适应决策方法。利用随机森林(RF)分别建立地质类型、操作参数与推进速度、刀盘转矩的映射关系模型;结合映射关系模型,构建以盾构机推进速度最大为目标,以刀盘转速、螺旋输送机转速、总推力、土仓压力4个操作参数为控制变量的优化方程;利用粒子群算法(PSO)求解各地质类型地层中的最优操作参数决策结果。通过新加坡某地铁工程施工数据验证所提方法的有效性和优越性。研究结果表明:建立的随机森林模型中推进速度和刀盘转矩预测的决定系数R^(2)分别达到0.936和0.961,均大于adaboost、多元线性回归、岭回归、支持向量回归和深度神经网络模型中相应的R2;基于粒子群算法的操作参数决策方法能够准确求解操作参数最优解,寻优用时均比遗传算法、蚁群算法和穷举法的短。本文所提决策方法使隧道掘进机在该施工段的福康宁卵石地层、句容地层IV、句容地层V、海洋黏土地层中的推进速度分别提升了67.2%、41.8%、53.6%和15.0%。

关键词：隧道掘进机操作参数决策随机森林粒子群优化

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基于长短时记忆网络的盾构机刀盘扭矩实时预测

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地下空间与工程学报 2020年第6期16卷 1801-1808页

作者：杨泰春陶建峰余宏淦刘成良上海交通大学机械与动力工程学院上海200240

盾构机是土质隧道开挖的优质工程机械,被广泛应用在地铁建设。刀盘扭矩是保证盾构正常推进的关键参数,能被精确实时预测对预防灾难事故、确保施工正常推进具有极高的指导意义。针对现有扭矩预测多为计算平均值的问题,提出一种基于长短... 详细信息

盾构机是土质隧道开挖的优质工程机械,被广泛应用在地铁建设。刀盘扭矩是保证盾构正常推进的关键参数,能被精确实时预测对预防灾难事故、确保施工正常推进具有极高的指导意义。针对现有扭矩预测多为计算平均值的问题,提出一种基于长短时记忆(Long-Short Term Memory,LSTM)网络的扭矩实时预测模型。首先通过分析盾构机状态参数与扭矩的相关性,选择一组关键状态参数,降低输入维度;然后建立LSTM扭矩预测模型;最后利用该模型在归一化后的实际数据集上进行验证并与BP网络模型对比。试验结果表明,该模型在测试集上均方差为0.002 81,平均绝对误差为0.036 7,均优于BP网络;该模型具有良好的预测能力与泛化性能,能够很好地拟合关键状态参数与刀盘扭矩之间的非线性关系。

关键词：盾构机刀盘扭矩实时预测长短时记忆网络

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