摘要
随着传感与智能化技术不断发展,隧道掘进机的运行监测日趋完善,所记录的海量实测数据不仅包含了装备作业过程的重要信息,也蕴含了装备内部及其与外部环境的相互作用机理,通过一定方法对这些数据进行深度挖掘与分析对于提升装备设计、分析、运行与维护水平具有十分重要的意义。为总结和分析隧道掘进机实测数据研究方法与应用状况,首先概述隧道掘进机实测数据的来源、构成与特点,从数据驱动的装备状态识别与性能预测、地质识别与地表改变预测、隧道健康监测与预警三个方面综述国内外相关文献,总结和归纳当前研究的难点、优点与不足,最后从隧道掘进机实测数据预处理方法、多源异构数据建模方法、模型泛化能力提升方法、数据计算平台等方面对未来研究方向进行初步分析与展望,为后续隧道掘进机大数据研究提供参考与借鉴。
With the continuous development of sensing and intelligent technology,the operational monitoring of tunnel boring machines is becoming increasingly perfect.The massive measured in-situ data not only record the important information of the operation process of tunnel boring machine but also involve the internal mechanism of tunnel boring machine and the external mechanism with the working environment.It is of great significance in improving the design,analysis,operation,and maintenance level of tunnel boring machine through mining these in-situ data.In order to summarize and analyze the research and application status of in-situ data of tunnel boring machine,the source,composition,and characteristics of in-situ data of tunnel boring machine are discussed first.Then,the domestic and foreign literature are reviewed from three aspects:data-driven state recognition and performance prediction of tunnel boring machine,data-driven geology recognition and ground surface change prediction and tunnel health monitoring and early warning.The difficulties,advantages,and deficiencies of current researches are discussed as well.Finally,the preliminary analysis and prospects on the future research directions are made from the aspects of in-situ data preprocessing methods,heterogeneous in-situ data modeling methods,generalization ability improvement methods of in-situ model,computing platform,and so on.It is expected to provide inspiration and references for the follow-up big data studies of tunnel boring machine.
作者
石茂林
孙伟
宋学官
SHI Maolin;SUN Wei;SONG Xueguan(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024;School of Agricultural Engineering,Jiangsu University,Zhenjiang 212013)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2021年第22期344-358,共15页
Journal of Mechanical Engineering
基金
国家重点研发计划资助项目(2018YFB1702502)
关键词
隧道掘进机
大数据
数据挖掘
数据驱动技术
tunnel boring machine
big data
data mining
data-driven techniques
