基于机器学习的常压刀盘盾构滚刀故障诊断方法研究  

Fault Diagnosis Method of Disc Cutters of Atmospheric Shield Cutterhead Based on Machine Learning

作　　者：孙浩 贾连辉[1] 魏晓龙 林福龙 孟祥波 SUN Hao;JIA Lianhui;WEI Xiaolong;LIN Fulong;MENG Xiangbo(China Railway Engineering Equipment Group Co.,Ltd.,Zhengzhou 450000,Henan,China;School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450000,Henan,China)

机构地区：[1]中铁工程装备集团有限公司,河南郑州450000 [2]郑州大学机械与动力工程学院,河南郑州450000

出　　处：《隧道建设（中英文）》2023年第S01期550-557,共8页Tunnel Construction

摘　　要：盾构在掘进过程中面临着极其复杂的地质条件,刀具极易损坏,而现有的刀具故障诊断方法无法对刀具故障进行全面、准确的判断。针对现有刀具故障诊断方法的不足,分析SVM、BPNN、ELM、RF等机器学习算法的原理及特点,研究常压刀盘盾构滚刀常见的磨损超限、卡转和偏磨等故障机制及特征,设计基于机器学习的常压刀盘盾构滚刀故障诊断模型,对滚刀故障进行分层诊断。选取对于滚刀故障较为敏感的温度、瞬时转速比、平均转速比等参数,使用盾构掘进现场数据进行试验,用于判断滚刀是否发生故障的第1层SVM、BPNN、ELM和RF算法模型准确率分别达到87.49%、88.69%、78.27%和96.45%,当滚刀发生故障时,用于判断滚刀具体故障形式的第2层SVM、BPNN、ELM和RF算法模型准确率分别达到90.24%、86.76%、79.41%和97.06%。验证了基于机器学习的常压刀盘盾构滚刀故障诊断模型的科学性和有效性,以及RF算法在判断滚刀是否发生故障以及发生故障后滚刀的故障类型具有较高的准确率,能够有效降低企业施工、换刀成本,提高盾构掘进效率。The cutters of shield cutterhead are prone to damage due to complex geologies encountered,and the existing cutter fault diagnosis methods cannot comprehensively and accurately judge the cutter conditions.Therefore,the principles and characteristics of machine learning algorithms such as support vector machine(SVM),back propagation neural network(BPNN),extreme learning machine(ELM),and random forest(RF)are analyzed,the principle and characteristics of common faults such as wear overrun,stuck rotation,and eccentric wear of the disc cutter in atmospheric shield cutterhead are examined,and a fault diagnosis model of atmospheric shield cutterhead disc cutter based on machine learning is designed to conduct hierarchical fault diagnosis.The temperature,instantaneous speed ratio,and average speed ratio that are more sensitive to the disc cutter failure are selected,and the shield tunneling field data are used to conduct experiments,so as to determine whether the disc cutters fail.The accuracies of the first-layer SVM,BPNN,ELM,and RF algorithm models reach 87.49%,88.69%,78.27%,and 96.45%,respectively.When the disc cutters fail,the accuracies of the second-layer SVM,BPNN,ELM,and RF algorithm models reach 90.24%,86.76%,79.41%,and 97.06%,respectively.These validate the rationality and effectiveness of the designed machine learning-based atmospheric shield cutterhead disc cutter fault diagnosis model.It is found that the RF algorithm has a high accuracy in judging whether the disc cutters fail and the fail type.Thus,the cost of enterprise construction and tool change can be effectively reduced,and the tunneling efficiency of the shield can be improved.

关 键 词：盾构 滚刀 常压刀盘 机器学习 故障诊断 

分 类 号：U45[建筑科学—桥梁与隧道工程]