关联规则挖掘驱动的盾构机刀盘健康评估方法

doi:10.3969/j.issn.1004-132X.2023.11.008

摘要/Abstract

摘要： 传统机理建模研究与实际施工环境误差较大，而数据驱动建模多采用黑箱模型，不利于知识发现与理解，为此提出一种基于知识挖掘的盾构机刀盘健康评估方法。针对盾构掘进数据维数众多、海量异构、强噪声干扰等特点，结合盾构掘进领域知识与机器学习算法提出针对性的数据预处理、特征筛选以及连续特征离散化方法，以此建立知识挖掘数据集。在此基础上，利用关联规则挖掘算法提取关键特征与不同刀盘健康状态之间的映射关系，采用融合可靠度、完整度与简洁度的综合评价指标适应度准则对原始规则进行评价排序，最终实现盾构机刀盘健康评估。基于厦门地铁3号线某掘进区间的实际工程数据对所提方法进行了应用验证。研究结果表明，所挖掘的知识规则与实际数据分布具有良好的吻合度（平均值93%以上），验证了该方法的有效性。

关键词: 盾构机刀盘, 健康评估, 关联规则, 数据挖掘, 知识挖掘

Abstract: Traditional mechanism modeling method had large errors with the actual construction environment, whereas data-driven modeling mostly used black-box models, which was not conducive to knowledge discovery and understanding, therefore a knowledge mining-based shield machine cutterhead health assessment method was proposed. For the characteristics of shield excavation data with many dimensions, massive heterogeneity and strong noise interference, specific data pre-processing, feature screening and continuous feature discretization methods were proposed to establish a knowledge mining dataset combining the domain knowledge of shield excavation and machine learning algorithms. Then, the association rule mining algorithm was used to extract the mapping relationship among key features and different cutterhead health levels. The original rules were evaluated and ranked by using a comprehensive evaluation index that integrated reliability, completeness and simplicity to finally realize the shield machine cutterhead health assessment. The proposed method was validated based on the actual engineering data of one tunneling section of Xiamen Metro Line 3. The results show that the mined knowledge rules have a good agreement with the actual data distribution (average 93% or more), which verifies the effectiveness of the method.

Key words: shield machine cutterhead, health assessment, association rule, data mining, knowledge mining

中图分类号:

TH17
U455

刘尧, 陈改革, 刘振国, 孔宪光, 常建涛. 关联规则挖掘驱动的盾构机刀盘健康评估方法[J]. 中国机械工程, 2023, 34(11): 1326-1334,1342.

LIU Yao, CHEN Gaige, LIU Zhenguo, KONG Xianguang, CHANG Jiantao. Health Assessment Method of Shield Machine Cutterheads Driven by Association Rule Mining[J]. China Mechanical Engineering, 2023, 34(11): 1326-1334,1342.

参考文献

［1］周建军, 宋佳鹏, 谭忠盛. 砂卵石地层地铁盾构盘形滚刀磨蚀性研究［J］. 土木工程学报, 2017,50(增刊1):31-35.
ZHOU Jianjun, SONG Jiapeng, TAN Zhongsheng. Study on Abrasive Properties of Shielded Hob in Subway Shield of Sandy Gravel Formation［J］. China Civil Engineering Journal, 2017,50(S1):31-35.
［2］张晓波, 刘泉声, 张建明. TBM掘进刀具磨损实时监测技术及刀盘振动监测分析［J］. 隧道建设, 2017,37(3):380-385.
ZHANG Xiaobo, LIU Quansheng, ZHANG Jianming. Real-time Monitoring Technology for Wear of Cutters and Monitoring and Analysis of Cutterhead Vibration of TBM［J］. Tunnel Construction, 2017,37(3):380-385.
［3］刘招伟, 王百泉, 尚伟. 基于超声波的盾构切刀磨损无线检测系统研究［J］. 隧道建设, 2017,37(11):1469-1474.
LIU Zhaowei, WANG Baiquan, SHANG Wei. Study of Wireless Detection System of Shield Cutter Wear Based on Ultrasonic［J］. Tunnel Construction, 2017, 37(11):1469-1474.
［4］ZHUO Xingjian, LU Yati. Real-time Wear Monitoring System for Scrape Cutters and Tearing Cutters［J］. Tunnel Construction, 2018,38(6):1060-1065.
［5］LAN Hao, XIA Yimin, JI Zhiyong, et al. Online Monitoring Device of Disc Cutter Wear—Design and Field Test［J］. Tunnelling and Underground Space Technology, 2019,89:284-294.
［6］SUN Zhenchuan, ZHAO Hailei, HONG Kairong, et al. A Practical TBM Cutter Wear Prediction Model for Disc Cutter Life and Rock Wear Ability［J］. Tunnelling and Underground Space Technology, 2019,85:92-99.
［7］REN D J, SHEN S L, ARULRAJAH A, et al. Prediction Model of TBM Disc Cutter Wear during Tunnelling in Heterogeneous Ground［J］. Rock Mechanics and Rock Engineering, 2018,51:3599-3611.
［8］WANG Lihui, LI Haipeng, ZHAO Xiangjun, et al. Development of a Prediction Model for the Wear Evolution of Disc Cutters on Rock TBM Cutterhead［J］. Tunnelling and Underground Space Technology, 2017,67:147-157.
［9］MASOUD Z N, MASOUD S, MASOUD R, et al. State-of-the-art Predictive Modeling of TBM Performance in Changing Geological Conditions through Gene Expression Programming［J］. Measurement, 2018,126:46-57.
［10］QIN Chengjin, SHI Gang, TAO Jianfeng, et al. Precise Cutterhead Torque Prediction for Shield Tunneling Machines Using a Novel Hybrid Deep Neural Network［J］. Mechanical Systems and Signal Processing, 2021,151:107386.
［11］ZHANG Qianli, HU Weifei, LIU Zhenyu, et al. TBM Performance Prediction with Bayesian Optimization and Automated Machine Learning［J］. Tunnelling and Underground Space Technology, 2020,103:103493.
［12］石茂林, 孙伟, 宋学官. 隧道掘进机大数据研究进展：数据挖掘助推隧道挖掘［J］. 机械工程学报, 2021,57(22):344-358.
SHI Maolin, SUN Wei, SONG Xueguan. Research Progress on Big Data of Tunnel Boring Machine:How Data Mining Can Help Tunnel Boring［J］. Journal of Mechanical Engineering, 2021,57(22):344-358.
［13］ELBAZ K, SHEN S L, ZHOU A N, et al. Prediction of Disc Cutter Life during Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network［J］. Engineering, 2021,7(2):238-251.
［14］YU Honggan, TAO Jianfeng, HUANG Sheng, et al. A Field Parameters-based Method for Real-time Wear Estimation of Disc Cutter on TBM Cutterhead［J］. Automation in Construction, 2021,124:103603.
［15］杨延栋, 孙振川, 张兵, 等. 基于多个隧道掘进机工程数据回归分析的滚刀磨损评价方法［J］. 中国机械工程, 2021,32(11):1370-1376.
YANG Yandong, SUN Zhenchuan, ZHANG Bing, et al. Disc Cutter Wear Evaluation Method Based on Regression Analysis of Multiple TBM Engineering Data［J］. China Mechanical Engineering, 2021,32(11):1370-1376.
［16］张康, 黄亦翔, 赵帅, 等. 基于 t-SNE 数据驱动模型的盾构装备刀盘健康评估［J］. 机械工程学报, 2019,55(7):19-26.
ZHANG Kang, HUANG Yixiang, ZHAO Shuai, et al. Health Assessment of Shield Equipment Cutterhead Based on t-SNE Data-driven Model［J］. Journal of Mechanical Engineering, 2019,55(7):19-26.
［17］AGRAWAL A K, MURTHY V M S R, CHATTOPADHYAYA S, et al. Prediction of TBM Disc Cutter Wear and Penetration Rate in Tunneling through Hard and Abrasive Rock Using Multi-layer Shallow Neural Network and Response Surface Methods［J］. Rock Mechanics and Rock Engineering, 2022,55(6):3489-3506.
［18］KARTHIKEYAN T, RAVIKUMAR N. A Survey on Association Rule Mining［J］. International Journal of Advanced Research in Computer and Communication Engineering, 2014,3(1):2278-1021.
［19］YANG Z, TANG W H, SHINTEMIROV A, et al. Association Rule Mining-based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers［J］. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2009,39(6):597-610.
［20］张天瑞, 于天彪, 赵海峰，等. 数据挖掘技术在全断面掘进机故障诊断中的应用［J］. 东北大学学报 (自然科学版), 2015,36(4):527-532.
ZHANG Tianrui, YU Tianbiao, ZHAO Haifeng， et al. Application of Data Mining Technology in Fault Diagnosis of Tunnel Boring Machine［J］. Journal of Northeastern University(Natural Science), 2015,36(4):527-532.
［21］LI Y, WANG J, DUAN L, et al. Association Rule-based Feature Mining for Automated Fault Diagnosis of Rolling Bearing［J］. Shock and Vibration, 2019,2019:1518246.
［22］陈勇刚, 孙向东, 崔丽娟，等. 基于关联规则挖掘的航空设备故障诊断研究［J］. 数学的实践与认识, 2021,51(9):99-107.
CHEN Yonggang, SUN Xiangdong, CUI Lijuan, et al. Study on Fault Diagnosis of Aviation Equipment Based on Association Rules Mining［J］. Mathematics in Practice and Theory, 2021,51(9):99-107.［23］KAUSHIK M, SHARMA R, PEIOUS S A, et al. A Systematic Assessment of Numerical Association Rule Mining Methods［J］. SN Computer Science, 2021,2(5):348.
［24］SAXENA A, RAJPOOT V. A Comparative Analysis of Association Rule Mining Algorithms［J］. IOP Conference Series:Materials Science and Engineering, 2021, 1099:012032.
［25］KE G, MENG Q, FINLEY T, et al. LightGBM:A Highly Efficient Gradient Boosting Decision Tree［J］. Advances in Neural Information Processing Systems, 2017,30:3146-3154.
［26］GARCIA S, LUENGO J, SAEZ J A, et al. A Survey of Discretization Techniques:Taxonomy and Empirical Analysis in Supervised Learning［J］. IEEE Transactions on Knowledge and Data Engineering, 2013,25(4):734-750.
［27］FREITAS A A. A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery［M］∥GHOSH A, TSUTSUI S. Advances in Evolutionary Computing:Theory and Applications. Berlin:Springer Berlin Heidelberg, 2003:819-845.

[1]	单增海, 李志远, 张旭, 黄亦翔, 李彦明, 刘成良, 张轩. 基于多传感器信息融合和多粒度级联森林模型的液压泵健康状态评估[J]. 中国机械工程, 2021, 32(19): 2374-2382.
[2]	林文广, 赖荣燊, 肖人彬. 基于语义关联的专利有害性能知识挖掘研究[J]. 中国机械工程, 2021, 32(16): 1952-1962.
[3]	仇永涛1;吉卫喜1,2;张朝阳1,2. 结合混合进化算法和知识的多目标车间调度[J]. 中国机械工程, 2020, 31(24): 2979-2987.
[4]	李阳, 郭飞, 李茂源, 张云, 李德群. [成形过程的数据挖掘与深度学习方法]塑料注射成形智能技术及其应用[J]. 中国机械工程, 2020, 31(22): 2734-2744.
[5]	庞世宝1;郭顺生1;王磊1;李西兴2. 面向大规模个性化制造的车间制造服务协作链构建方法[J]. 中国机械工程, 2020, 31(17): 2104-2111.
[6]	叶永伟;程毅飞;赖剑人;任设东. 基于改进关联规则的卸船机故障预测模型[J]. 中国机械工程, 2019, 30(20): 2463-2472.
[7]	张雷, 钟言久, 袁远, 李璟, 秦旭, 董万富. [智能设计与计划调度]基于数据挖掘的绿色设计中客户需求向工程特性权重转化方法[J]. 中国机械工程, 2019, 30(02): 174-182.
[8]	李敏波, 丁铎, 易泳. [质量优化]基于FP-Growth改进算法的轮胎质量数据分析[J]. 中国机械工程, 2019, 30(02): 244-251.
[9]	贾子文;顾煜炯. 基于数据挖掘的风电机组齿轮箱运行状态分析[J]. 中国机械工程, 2018, 29(06): 650-658.
[10]	汪俊亮, 秦威, 张洁. 基于数据挖掘的晶圆制造交货期预测方法[J]. 中国机械工程, 2016, 27(01): 105-108.
[11]	杨述明, 邱静, 刘冠军. 面向装备健康管理的监测参数选择与健康评估方法研究[J]. 中国机械工程, 2012, 23(13): 1513-1517.
[12]	罗妤1, 2, 郭钢1, 徐建萍3. 基于多维关联规则的产品族配置研究 [J]. 中国机械工程, 2011, 22(19): 2330-2336.