面向盾构机不完整数据的模糊聚类与非线性回归填补

doi:10.3901/JME.2023.12.028

机械工程学报 ›› 2023, Vol. 59 ›› Issue (12): 28-37.doi: 10.3901/JME.2023.12.028

• 特邀专栏：制造大数据分析与决策 • 上一篇下一篇

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面向盾构机不完整数据的模糊聚类与非线性回归填补

王一棠¹, 庞勇¹, 张立勇², 史彦军¹, 孙伟¹, 宋学官¹

1. 大连理工大学机械工程学院大连 116024;
2. 大连理工大学控制科学与工程学院大连 116024

收稿日期:2022-12-12 修回日期:2023-05-25 出版日期:2023-06-20 发布日期:2023-08-15
通讯作者: 宋学官(通信作者),男,1982年出生,博士,教授,博士研究生导师。主要研究方向为多学科耦合建模与优化设计、工业大数据挖掘及数据驱动的预测技术、装备智能化与数字孪生。E-mail:sxg@dlut.edu.cn
作者简介:王一棠,女,1993年出生,博士研究生。主要研究方向为工业大数据挖掘及数据驱动的预测技术。E-mail:yitangwang@mail.dlut.edu.cn
基金资助:
国家重点研发计划资助项目(2018YFB702502)。

Fuzzy Clustering and Nonlinear Regression Imputation for Incomplete Data of Tunnel Boring Machine

WANG Yitang¹, PANG Yong¹, ZHANG Liyong², SHI Yanjun¹, SUN Wei¹, SONG Xueguan¹

1. School of Mechanical Engineering, Dalian University of Technology, Dalian 116024;
2. School of Control Science and Engineering, Dalian University of Technology, Dalian 116024

Received:2022-12-12 Revised:2023-05-25 Online:2023-06-20 Published:2023-08-15

摘要/Abstract

摘要： 实测数据的完整性和可靠性是工业大数据时代推动各行业发展的必要前提和关键因素。盾构机作为隧道掘进施工的关键设备，具有系统结构复杂，部分属性关联度高等特点，是“一带一路”等国家战略中城市地下建设的国之重器。然而，在盾构机作业过程中，由于环境干扰、采集中断、设备故障等诸多原因，数据缺失已成为盾构机实测数据采集中不可避免的问题，严重降低数据的质量与可信度，影响工程进度。针对盾构机实测数据的特点，提出一种基于模糊聚类和非线性回归的高精度缺失值填补算法。该方法首先通过模糊聚类的方法，将不同工况运行实测数据进行有效划分，获取多个线性子集。然后，为每个子集建立线性回归模型，采用交替迭代策略来求解模型参数，有效挖掘属性间的关联关系。试验结果表明，所提方法无论是在对不完整数据聚类，还是对缺失数据填补，都具有良好的表现。提出的数据填补算法可有效解决数据划分和恢复问题，为盾构机实测大数据挖掘提供可靠的基础。

关键词: 盾构机, 实测数据, 不完整数据填补, 模糊聚类, 数据建模

Abstract: The integrity and reliability of the in-situ data are the necessary prerequisites and key factors to promote the development of various industries in the era of industrial big data. As a key equipment of tunnel engineering, tunnel boring machine (TBM) has the characteristics of complex system structure and high relationship between the attributes, and it is a national tool for urban underground construction in national strategies such as “One Belt and One Road”. However, in the process of TBM operation, missing values frequently occurs in the acquisition of measured data of TBM due to various reasons, such as environmental interference, acquisition interruption, equipment failure, which seriously reduces the quality and reliability of data and affects the progress of the project. According to the characteristics of measured data of TBM, a high-precision missing value imputation algorithm based on fuzzy clustering and nonlinear regression is proposed. Firstly, the measured data under different working conditions are divided into several linear subsets by fuzzy clustering method. Then, a linear regression model is established for each subset, and an alternating learning strategy is used to solve the model parameters, which effectively mines the correlation between attributes. Experimental results show that the proposed method performs well both in clustering incomplete data and imputation missing data. The proposed data imputation algorithm can effectively solve the problem of data division and recovery, and provides a reliable foundation for the actual big data mining of shield machine.

Key words: tunnel boring machine, in-situ data, incomplete data imputation, fuzzy clustering, data modeling

中图分类号:

TP311
TH17

王一棠, 庞勇, 张立勇, 史彦军, 孙伟, 宋学官. 面向盾构机不完整数据的模糊聚类与非线性回归填补[J]. 机械工程学报, 2023, 59(12): 28-37.

WANG Yitang, PANG Yong, ZHANG Liyong, SHI Yanjun, SUN Wei, SONG Xueguan. Fuzzy Clustering and Nonlinear Regression Imputation for Incomplete Data of Tunnel Boring Machine[J]. Journal of Mechanical Engineering, 2023, 59(12): 28-37.

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面向盾构机不完整数据的模糊聚类与非线性回归填补

Fuzzy Clustering and Nonlinear Regression Imputation for Incomplete Data of Tunnel Boring Machine

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